Big Data and Analytics Training - United Kingdom

Big Data and Analytics Training

Big Data Analytics is the process of examining and uncovering meaningful insights from large and complex datasets to inform decision-making and gain valuable business intelligence. In today's data-driven world, Big Data Analytics is crucial for professionals to navigate the vast sea of information, make informed decisions, and gain a competitive edge in various industries, making training in this field essential for staying relevant and competitive. The Knowledge Academy offers comprehensive Big Data Analytics Training Courses, making it the ideal choice for individuals seeking top-quality education and expertise.

REASONS TO CHOOSE

Learn the latest data analysis methods and tools with our Big Data and Analytics Training
Gain expertise in handling massive datasets efficiently
Understand real-world applications for informed decisions
Learning this include high-demand job opportunities and competitive salaries
Harness the power of data to drive business decisions, ensuring a competitive edge

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Big Data and Analytics Training

Duration

Online Instructor-led (4 days)

Classroom (4 days)

Online Self-paced (32 hours)

Advanced Data Analytics Certification Course Outline

Day 1: Advanced Statistical Analysis and Data Engineering

Module 1: Advanced Data Analytics Foundations

Evolution of Data Analytics and Key Trends
Data Science vs. Data Analytics vs. Business Intelligence
Frameworks for Decision-Making with Data
Understanding Data Pipelines for Analytics

Module 2: Advanced Statistical Analysis and Hypothesis Testing

Probability Distributions (Normal, Binomial, Poisson)
Central Limit Theorem and Bayesian Inference
Hypothesis Testing (T-Test, ANOVA, Chi-Square)
Bootstrapping and Confidence Intervals

Module 3: Multivariate Analysis and Dimensionality Reduction

Principal Component Analysis (PCA)
Factor Analysis for Feature Selection
LDA for Classification Problems
Manifold Learning Techniques (t-SNE, UMAP)

Module 4: Advanced SQL for Data Analytics

SQL for Analytical Queries (Window Functions, CTEs)
Query Optimization and Performance Tuning
Time Series Data Manipulation in SQL
Recursive Queries and Advanced Joins

Day 2: Data Processing and Predictive Analytics

Module 5: Data Cleaning, Transformation and Feature Engineering

Handling Missing Data (Imputation and Outlier Detection)
Encoding Categorical Data (One-Hot, Target Encoding)
Feature Scaling and Normalization Techniques
Creating Synthetic Features for Better Models

Module 6: Exploratory Data Analysis (EDA) with Python

Data Profiling and Summary Statistics
Identifying Patterns and Outliers with Visualization
Correlation Analysis and Feature Relationships
Advanced Data Transformation Methods

Module 7: Predictive Analytics and Machine Learning Models

Regression Models: Linear, Ridge, Lasso
Classification Models: Logistic Regression, Decision Trees
Bias-Variance Tradeoff and Model Validation Strategies
Model Selection and Performance Metrics

Module 8: Ensemble Learning and Model Optimisation

Bagging (Random Forest) and Boosting (XGBoost, LightGBM, CatBoost)
Stacking and Blending Techniques
Hyperparameter Tuning (Grid Search, Bayesian Optimization)
Cross-Validation and Model Robustness Testing

Day 3: Unsupervised Learning and Time Series Forecasting

Module 9: Unsupervised Learning and Clustering Techniques

K-Means, DBSCAN, and Hierarchical Clustering
Anomaly Detection with Isolation Forests
Association Rule Mining (Apriori, FP-Growth)
Customer Segmentation for Business Insights

Module 10: Time Series Analysis and Forecasting

Understanding Trends and Seasonality
ARIMA, SARIMA, and Exponential Smoothing
Machine Learning for Time Series (XGBoost and LSTMs)
Anomaly Detection in Time Series Data

Module 11: Advanced Data Visualization Techniques

Customizing Visualizations with Matplotlib and Seaborn
Interactive Dashboards with Plotly
Advanced Charting Techniques (Heatmaps, Sankey Diagrams)
Best Practices for Data Storytelling

Module 12: Explainable AI and Model Interpretability

Feature Importance and SHAP Values
Partial Dependence Plots and LIME
Fairness and Bias Mitigation in Machine Learning
Ethical Considerations in AI Decision Making

Day 4: Business Intelligence and Real-World Applications

Module 13: Business Intelligence with Tableau

Data Preparation and Importing Large Datasets
Creating Advanced Charts and Visualizations
Interactive Dashboards and Storytelling in Tableau
Data Blending and Advanced Calculations

Module 14: Data-Driven Decision Making and Optimization Techniques

A/B Testing and Experimentation
Decision Trees for Strategic Decision-Making
Optimizing Business Processes with Data
Real-World Applications of Data Analytics

Module 15: Data Governance and Quality Management

Principles of Data Quality and Data Lineage
Data Cleaning Frameworks for Large-Scale Analytics
Handling Data Privacy and Compliance Regulations (GDPR, HIPAA)
Automating Data Validation and Monitoring

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Who should attend this Advanced Data Analytics Certification Course?

The Advanced Data Analytics Certification Training is designed for individuals aiming to delve deep into Data Analysis techniques, methodologies, and applications. This course can be beneficial for a wide range of professionals, including:

Data Scientists
Data Analysts
Business Analysts
Machine Learning Engineers
Business Intelligence Professionals
Database Administrators
Quantitative Researchers

Prerequisites of the Advanced Data Analytics Certification Course

There are no formal prerequisites for attending this Advanced Data Analytics Certification Course.

Advanced Data Analytics Certification Course Overview

Advanced Data Analytics stands at the forefront of enabling businesses and organisations to make data-driven decisions. By leveraging complex datasets, analytics techniques, and tools, professionals can uncover valuable insights, predict trends, and optimise operations, making it a critical skill in today’s data-centric world.

Proficiency in Advanced Data Analytics is crucial for data scientists, business analysts, and IT professionals who aim to excel in predictive analytics, extensive data analysis, and visualisation. Mastering this domain enables them to drive strategic decisions, enhance business outcomes, and maintain a competitive edge in the rapidly evolving digital landscape.

This 4-day training is designed to equip delegates with the knowledge and skills to apply advanced analytics techniques effectively. Participants will learn to navigate complex data, utilise modern analytics tools, and implement best practices in data analysis, preparing them for immediate application in real-world scenarios and advancing their careers.

Course Objectives

To understand the foundational and advanced principles of data analytics
To learn to apply statistical models and machine learning algorithms for predictive analytics
To gain proficiency in using leading analytics software and tools
To develop skills in data visualisation and interpretation of results for strategic decision-making
To enhance data handling capabilities, including big data platforms

After completing this Data Analytics Course, delegates will receive a certification, signifying their ability to handle complex analytics projects. This certification boosts career prospects, showcases expertise, and empowers data-driven contributions in organisations.

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What’s included in this Advanced Data Analytics Certification Course?

World-Class Training Sessions from Experienced Instructors
Advanced Data Analytics Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Certified Artificial Intelligence (AI) for Data Analysts Training Course Outline

Module 1: Introduction to AI for Data Analysts

Understanding Artificial Intelligence and Machine Learning
Key AI Concepts and Terminologies for Data Analysts
Role of AI in Modern Data Analysis

Module 2: AI-Driven Data Collection and Preprocessing

Automated Data Collection with AI Tools
Data Cleaning and Preprocessing Using AI
AI Techniques for Data Normalisation and Transformation
Practical Applications of AI in Data Preprocessing

Module 3: Machine Learning Fundamentals

Introduction to Machine Learning and Its Types
Supervised vs. Unsupervised Learning
Key Algorithms and Models in Machine Learning
Practical Applications of Machine Learning in Data Analysis

Module 4: Advanced AI Techniques in Data Analysis

Deep Learning and Neural Networks
Natural Language Processing (NLP) for Text Analysis
Computer Vision for Image and Video Analysis
Case Studies of Advanced AI Techniques in Data Analysis

Module 5: AI in Predictive and Prescriptive Analytics

Prescriptive Analytics and Decision Making in AI
AI for Forecasting and Trend Analysis
Practical Applications of Predictive and Prescriptive Analytics

Module 6: AI Tools and Frameworks for Data Analysts

Overview of Popular AI Frameworks and Libraries
Best Practices for Using AI Libraries and APIs
Enhancing Data Analysis with AI Tools

Module 7: AI for Data Visualisation and Reporting

AI-Driven Data Visualisation Techniques
Enhancing Reporting with AI-Generated Insights
Case Studies of AI in Data Visualisation and Reporting

Module 8: Ethical and Legal Considerations in AI for Data Analysis

Understanding Ethical Implications of AI in Data Analysis
Legal Framework and Compliance Issues
Responsible Use of AI in Data Practices
Future Trends and Challenges in AI for Data Analysts

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Who Should Attend Certified Artificial Intelligence (AI) for Data Analysts Training

This course is designed for data analysis professionals seeking to leverage AI in their data analysis practices. The ideal audience includes:

Data Analysts
Business Analysts
Data Scientists
Data Engineers
BI Analysts
Research Analysts
Data Visualisation Specialists

Prerequisites of Certified Artificial Intelligence (AI) for Data Analysts Training

There are no formal prerequisites for attending this AI for Data Analysts Training. However, having a basic understanding of data analysis principles, familiarity with data analysis tools and techniques, and an interest in artificial intelligence and machine learning applications would be beneficial for the delegates.

Certified Artificial Intelligence (AI) for Data Analysts Training Course Overview

The Certified Artificial Intelligence (AI) for Data Analysts training course is designed to equip data analysis professionals with the knowledge and skills to integrate AI into their data analysis practices. As AI continues to revolutionise data analysis, this course offers a comprehensive understanding of how AI can enhance data collection, preprocessing, analysis, visualisation, and reporting. By mastering these AI-driven techniques, participants can significantly advance their careers, opening up new opportunities in the rapidly evolving field of AI-enhanced data analysis.

This 1-day AI for Data Analysts Training course provide delegates with comprehensive knowledge of AI in data analysis, AI-driven data collection and preprocessing, machine learning fundamentals, advanced AI techniques, predictive and prescriptive analytics, AI tools and frameworks, data visualisation and reporting, and ethical and legal considerations. Delegates will gain practical knowledge on implementing AI tools and platforms, integrating AI with existing data analysis workflows, and following best practices for AI in data analysis. Real-world examples and case studies will demonstrate the transformative potential of AI in enhancing data analysis practices and improving overall data-driven decision-making.

Course Objectives

Understand the role and benefits of AI in modern data analysis practices
Learn to use AI for automated data collection and preprocessing
Develop skills to implement machine learning models and advanced AI techniques
Explore AI tools for predictive and prescriptive analytics
Gain insights into AI-driven data visualisation and interactive reporting
Discuss the ethical and legal implications of using AI in data analysis

After attending this training, delegates will be able to confidently integrate AI techniques into their data analysis practices. They will have the ability to automate complex pre-processing tasks, apply machine learning models to uncover hidden patterns, and utilise predictive and prescriptive analytics for better decision-making. Delegates will also be capable of leveraging AI tools and frameworks to enhance their reporting and visualisation outputs, ensuring greater clarity and accuracy in communicating insights.

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What's included in this Certified Artificial Intelligence for Data Analysts Training

World-Class Training Sessions from Experienced Instructors
The Certified Artificial Intelligence for Data Analysts Training
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Data Analytics with R Course Outline

Module 1: Introduction to Data Analytics and R

Understanding Data Analytics and Its Importance
Data Analytics Lifecycle and Phase
Types of Data Analysis
Applications of Data Analytics in Different Industries
Overview of R Programming for Data Analytics
Setting Up R and RStudio

Module 2: Data Handling and Preprocessing in R

Introduction to Data Handling and Preprocessing in R
Understanding Data Structures in R
Working with Vectors in R
Lists in R
Matrices and Arrays in R
Data Frames in R
Importing and Exporting Data in R
Handling Missing Values in R
Removing Duplicates in R
Identifying and Handling Outliers
Data Transformation Using dplyr
Data Tidying Using tidyr
Introduction to Exploratory Data Analysis (EDA)
Visualising Data in R for EDA
Summary and Best Practices in Data Preprocessing

Module 3: Business Intelligence and Analytics

Introduction to Business Intelligence and Data Visualisation in R
Fundamentals of Business Intelligence (BI) and Analytics
Data Visualisation Principles
Creating Static Visualisations with ggplot2
Interactive Dashboards with Shiny
Enhancing Visualisations with Plotly
Data Storytelling and Reporting
Best Practices in BI and Data Visualisation

Module 4: Statistical Analysis and Hypothesis Testing

Introduction to Statistical Analysis in R
Descriptive Statistics –Overview
Understanding Measures of Central Tendency
Understanding Measures of Dispersion
Visualising Data with Histograms and Boxplots
Inferential Statistics – Confidence Intervals
Introduction to Hypothesis Testing
Correlation and Covariance Analysis
Understanding Probability Distributions
Normality Testing in R
t-Tests in R
ANOVA (Analysis of Variance) in R
Chi-Square Test for Categorical Data
Visualising Statistical Results

Module 5: Machine Learning

Introduction to Machine Learning in R
Applications of Machine Learning
Supervised vs. Unsupervised Learning
Understanding Supervised Learning
Understanding Unsupervised Learning
Importance of Feature Engineering
Handling Missing Values in ML
Splitting Data for Training and Testing
Model Evaluation Metrics for Regression
Model Evaluation Metrics for Classification
Hyperparameter Tuning in Machine Learning
Cross-Validation for Reliable Models
Overfitting vs. Underfitting in ML Models
Comparing Different ML Models in R
Best Practices in ML with R

Module 6: Regression and Classification Models in R

Introduction to Regression and Classification Models in R
Linear Regression
Evaluating Linear Regression Models
Logistic Regression
Evaluating Logistic Regression Models
Decision Trees
Random Forest
Support Vector Machines (SVM)
k-Nearest Neighbours(kNN)
Handling Imbalanced Data in Classification Models

Module 7: Unsupervised Learning and Advanced Analytics

Introduction to Unsupervised Learning and Advanced Analytics
Clustering Techniques
k-Means Clustering in R
Hierarchical Clustering in R
Principal Component Analysis (PCA) for Dimensionality Reduction
Association Rule Mining – Apriori Algorithm
Time Series Analysis
Time Series Forecasting with ARIMA in R
Text Mining and Sentiment Analysis
Sentiment Analysis in R
Summary and Best Practices in Unsupervised Learning & Advanced Analytics

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Who should attend this Data Analytics with R Course?

The Data Analytics with R Course is designed for individuals seeking to harness the power of data to make informed decisions. This course offers insights and tools to propel your analytical capabilities. This course can be beneficial for a wide range of professionals, including:

Data Analysts
Statisticians
Business Analysts
Financial Analysts
Marketing Analysts
Researchers
Healthcare Data Professionals
IT and Software Engineers

Prerequisites of the Data Analytics with R Course

There are no formal prerequisites for this Data Analytics with R Course.

Data Analytics with R Course Overview

Data Analytics with R Course is a crucial component of Big Data and Analytics Training. In today's data-driven world, the power of R for data analysis is paramount. R, a programming language and environment widely used for statistical analysis and data visualisation, holds the key to uncovering actionable insights from vast datasets. Its relevance is evident in its ability to aid professionals in making informed decisions based on data-driven findings.

Proficiency in R is the ability to harness R's capabilities, enabling them to extract meaningful patterns from complex datasets, aiding in informed decision-making. For instance, data scientists proficient in R can explore trends and uncover hidden insights in various industries, making it a critical skill for career advancement.

The 1-day course offered by the Knowledge Academy is designed to empower delegates with the knowledge and practical skills they need for data analytics with R. Through hands-on experience in data manipulation, statistical analysis, and data visualisation using R, participants will gain a deep understanding of the subject. The course emphasizes real-world applications, ensuring that delegates are well-equipped to tackle data-related challenges effectively.

Course Objectives

To gain expertise in exploratory data analysis and visualisation techniques
To acquire knowledge of advanced statistical methods for data interpretation
To understand machine learning algorithms and their application in data analytics
To learn to create interactive dashboards for data presentation
To implement real-time analytics solutions using R and Big Data technologies
To apply ethical considerations and best practices in data analytics projects

After completing this Big Data and Analytics Course, you will receive a certification, validating your proficiency in R for data analytics. This certification serves as a tangible proof of your skills and enhances your career prospects.

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What’s included in this Data Analytics with R Course?

World-Class Training Sessions from Experienced Instructors
Data Analytics with R Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Data Science Analytics Course Outline

Module 1: Introduction to Data Science

What is Data Science?
Types of Data
Data Science Pipeline

Module 2: Understanding Data Wrangling

Data Wrangling Workflow
Data Acquisition
Five Steps of the Data Collection Process
Data Enriching
Data Cleansing

Module 3: Data Analysis

Data Analysis within Business
Confirmatory Data Analysis
Exploratory Data Analysis
Data Analysis Files

Module 4: Data Mining

Introduction to Data Mining
- Common Classes of Tasks Under Data Mining
Regression Analysis

Module 5: Understanding Data Visualisation

Introduction to Data Visualisation
- Six Principles of Data Visualisation
- Elements of Data Visualisation
Psychology of Charts

Module 6: Data Manipulation

Data Manipulation Overview
Types of Structuring Involved in Data Manipulation
- Intrarecord Structuring
- Interrecord Structuring

Module 7: Working with Large Amounts of Data

What is Big Data?
- Different Devices and Applications
Fundamentals of Big Data
- 3 V’s
- Sources of Big Data
Data Tools
Structure
Sampling
- Methods of Sampling
Chunking Principles
- How Big Should Data Chunks Be?

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Who should attend this Data Science Analytics Course?

This Data Science Analytics Course is suitable for a wide range of individuals looking to enhance their skills and knowledge in the field of Data Science and Analytics. This Big Data and Analytics Course can be beneficial for a wide range of professionals, including:

Data Analysts
Business Analysts
Data Scientists
IT Professionals
Managers
Entrepreneurs
Financial Analysts

Prerequisites of the Data Science Analytics Course

There are no formal prerequisites for this Data Science Analytics Course.

Data Science Analytics Course Overview

Introduction to the Data Science Analytics Course reveals the significance of harnessing the power of Big Data and Analytics. In an era defined by information, businesses and professionals who can derive insights from vast datasets gain a competitive edge, making Big Data Analytics Courses a pivotal field of study.

Proficiency in Big Data Analytics Courses is vital for professionals across various domains, including Business, Finance, Healthcare, and Technology. It empowers them to extract valuable information from vast datasets, enhancing decision-making and driving organisational success. Anyone aspiring to excel in their respective fields should aim to acquire proficiency in this subject.

This intensive 1-day training provides a comprehensive introduction to Big Data and Analytics, equipping delegates with the fundamental skills needed for data analysis. Through practical applications and real-world case studies, participants will gain hands-on experience in data handling, interpretation, and visualisation.

Course Objectives

To understand the fundamentals of data science and analytics
To learn data collection, cleaning, and preparation techniques
To develop proficiency in data visualisation and interpretation
To gain insights into machine learning and predictive analytics
To explore the impact of Big Data on business strategies
To enhance decision-making through data-driven insights

After completing this course, delegates will receive a certification in Big Data Analytics Courses, validating their expertise in data science and analytics. This certification serves as a valuable asset, opening doors to new career opportunities and enhancing professional growth.

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What’s included in this Data Science Analytics Course?

World-Class Training Sessions from Experienced Instructors
Data Science Analytics Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Data Analysis Training using MS Excel Course Outline

Module 1: Overview of Data Analysis

What is Data Analysis?
Why Data Analysis?
Types of Data Analysis
Data Analysis Process

Module 2: Introduction to Data Analysis with MS Excel

Introduction to Excel Data Analysis
Data Cleaning
Data Analysis
Data Visualisation

Module 3: Excel Ribbon and Importing Data into Excel

Excel Ribbons
Importing Data into Excel

Module 4: Work with Range Names

Steps to Create Range Name
How to Rename Range Name?
How to Delete Range Name?
Use Name Range in Workbook

Module 5: Introduction to Tables

What is a Table?
What is the Purpose of Creating a Table?

Module 6: Cleaning Data with Text Functions

Removing Unwanted Characters from the Text

Module 7: Working with Date Formats and Time Formats

Steps to Change Data Format
Steps to Change Time Format

Module 8: Conditional Formatting in Excel

What is Conditional Formatting and How to Use It?
Apply Conditional Formatting on Text

Module 9: Sorting and Filtering Data Columns

What is Sorting and Filtering?
Sort a Particular Column
Applying Sorting on Two Columns
Steps to Sort Dates
Clear Filter
Apply Filter by Cell Icon

Module 10: Subtotals and Quick Analysis

Subtotals
Steps to Apply Subtotals
Quick Analysis
Steps to Use Quick Analysis

Module 11: Working with Multiple Sheets

Worksheet Tab
Viewing Multiple Worksheets at Once
Grouping Your Worksheets Together
Steps to Rename a Worksheet
Steps to Move/Copy a Worksheet
Steps to Delete a Worksheet

Module 12: Data Validation

What is Data Validation?
Using Data Validation?

Module 13: Data Visualisation

What is Data Visualisation?
Using Charts in Excel
All Charts in Excel

Module 14: Exploring Lookup Functions

Lookup Function
VLOOKUP and HLOOKUP
INDEX Function
MATCH Function

Module 15: Pivot Tables

PivotTable Overview
Creating a PivotTable in MS Excel
Recommended PivotTables
PivotTable Areas
Summarising Values by Other Calculation
Using ANALYSE and DESIGN on the Ribbon

Module 16: What If Analysis

What If Analysis
What If Analysis with Data Tables
What If Analysis with Scenario Manager
What If Analysis with Goal Seek

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Who should attend this Data Analysis Training using MS Excel Course?

This Data Analysis Training using MS Excel is suitable for a diverse range of individuals looking to enhance their analytical skills. This Big Data and Analytics can be beneficial for a wide range of professionals, including:

Data Analysts
Business Analysts
Financial Analysts
Market Research Analysts
Operations Analysts
Marketing Analysts
Risk Analysts
Reporting Analysts
Business Intelligence Analysts

Prerequisites of the Data Analysis Training using MS Excel Course

There are no formal prerequisites for this Data Analysis Training using MS Excel Course. However, basic knowledge of MS Excel would be beneficial for the delegates.

Data Analysis Training using MS Excel Course Overview

The Data Analysis Training using MS Excel Course provides a crucial stepping stone into the world of data analysis, empowering delegates to unlock valuable insights from datasets. With the prevalence of data-driven decision-making across various industries, this training is more relevant than ever. As organisations harness the power of data to make informed decisions, individuals must equip themselves with the right skills.

Proficiency in data analysis is paramount for professionals from diverse backgrounds. From Business Analysts to marketing managers and financial planners to healthcare administrators, anyone seeking to harness the potential of data can benefit from mastering this subject. Competence in data analysis becomes essential for career advancement and staying competitive in the job market.

This intensive 2-day Data Analysis Training using MS Excel equips delegates with practical skills to manipulate and analyse data effectively using MS Excel. Delegates will gain the ability to clean and preprocess data, create insightful visualisations, and draw meaningful conclusions from their analysis. The course fosters a hands-on approach, ensuring that delegates leave with actionable skills that can be applied immediately in their professional roles.

Course Objectives

To develop the skills to create informative data visualisations
To gain insights into statistical analysis and hypothesis testing
To master pivot tables and data modelling for decision support
To understand how to make data-driven recommendations
To explore real-world case studies and practical applications
To enhance problem-solving skills through data analysis challenges

After completing this Big Data and Analytics Course, delegates will receive a certification in Data Analysis Training using MS Excel. This certification not only validates their newly acquired skills but also enhances their professional credibility.

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What’s included in this Data Analysis Training using MS Excel Course?

World-Class Training Sessions from Experienced Instructors
Data Analysis Training using MS Excel Certificate
Digital Delegate Pack

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Explore Course Information

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Data Analysis and Visualisation with Python Course Outline

Module 1: Introduction to Data Science with Python

Stages of Data Science
Why Python?
Python Environment and Editors
Data Cleaning and Manipulation Techniques

Module 2: Importance of Data Visualisation in Business Intelligence

Importance of Data Visualisation
Why do Modern Businesses Need Data Visualisation?
Future of Data Visualisation
Data Visualisation Techniques

Module 3: Data Collection Structures

Lists
Dictionaries
Tuples

Module 4: File I/O Processing and Regular Expressions

File I/O Processing
Regular Expressions

Module 5: Data Exploring and Analysis

Series Data Structures
Data Frame and Structures

Module 6: Data Visualisation

Direct Plotting
Matplotlib Plot

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Who should attend this Data Analysis and Visualisation with Python Course?

The Data Analysis and Visualisation with Python Course is an exhaustive course intended to equip professionals, students, and data enthusiasts with the necessary skills to extract valuable insights from data using Python. The following are some professionals who can benefit from this course:

Data Analysts
Data Scientists
Business Analysts
Software Engineers
Financial Analysts
Project Managers
Business Intelligence Professionals
Market Researchers

Prerequisites of the Data Analysis and Visualisation with Python Course

There are no formal prerequisites for Data Analysis and Visualisation with Python Course.

Data Analysis and Visualisation with Python Course Overview

Data analysis is the procedure of adding structure and order to collect data that teams can use effectively. Data visualisation is the method of putting data into graphs, charts, or other visual formats that assists in informing interpretation and analysis. It provides a better understanding of what the data means by providing visual context in the form of maps or graphs. Studying Data Analysis and Visualisation with Python will help learners to develop the management, analytical, and design skills needed to translate complex and large datasets into understandable visual forms. It helps organisations and stakeholders in examining reports concerning product interest, sales, and marketing strategies. Having Data Analysis and Visualisation with Python skills will help individuals to undertake a variety of tremendous job opportunities.

This 1-day Data Analysis and Visualisation with Python Training course cover all the essential topics to get thoroughly familiar with the basic and advanced concepts of Data Analysis and Visualisation with Python. During this training, delegates will learn about the importance of data visualisation in business intelligence. They will also learn about series data structures, data frame data structures, data analysis, data visualisation techniques, abstraction of the series and data frame, and many more. Our highly professional trainer with years of experience in teaching Python courses will conduct this training course and help you get a comprehensive understanding of data analysis and visualisation.

Course Objectives

To understand data science stages using Python programming
To apply data cleaning and manipulation techniques effectively
To explore importance of data visualisation in business intelligence
To conduct exploratory data analysis with series and data frames
To develop effective data visualisations using Python libraries
To present data-driven insights through clear visual representation

After attending this Data Analysis and Visualisation with Python Training course, delegates will be able to utilise data visualisation for better business decision-making. They will also be able to effectively read the data from JSON, HTML, and XML format.

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What’s included in this Data Analysis and Visualisation with Python Course?

World-Class Training Sessions from Experienced Instructors
Data Analysis and Visualisation with Python Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Hadoop Big Data Certification Training Course Outline

Module 1: Understanding Hadoop

What is Web Hadoop?
Why is Hadoop Important?
Hadoop Architecture
Challenges of Using Hadoop

Module 2: Processing Distributed Data

HDFC
MapReduce

Module 3: Introduction to Data Storage and Processing

Overview
Projects for Structured Data Storage and Processing

Module 4: Defining Hadoop Cluster Requirements

Hadoop Cluster
Advantages
Hadoop Cluster Architecture
Best Practices for Building Hadoop Cluster

Module 5: Configuring a Cluster

Types of Configuration Files Drive Hadoop Configuration
Code Example

Module 6: Maximising HDFS Robustness

Three Types of Failures in HDFS
Data Disk Failure, Heartbeats, and Re-Replication
Cluster Rebalancing
Data Integrity
Metadata Disk Failure
Snapshots

Module 7: Managing Resources and Cluster Health

Managing Resources
Managing HDFS Cluster
Secondary NameNode Configuration
MapReduce Cluster Management

Module 8: Maintaining a Cluster

FileSystem Checks
HDFS Balancer Utility
Add New Nodes to Cluster
Decommissioning a Node from Cluster
Datanode Volume Failures
Database Backups
HDFS Metadata Backup
Purging Older Log Files

Module 9: Extending Hadoop and Implementing Data Ingress

Extending Hadoop Towards Data Lake
Hadoop Built-in Ingress and Egress Tools

Module 10: Planning for Backup, Recovery, and Security

Introduction to Backup and Recovery
Goals and Objectives

Module 11: Introduction to Big Data

What is Big Data?
Three V
Sources of Big Data

Module 12: Storing Big Data

Introduction to Big Data Storage
Key Requirements of Big Data Storage
Big Data Storage Architectures

Module 13: Processing Big Data

Introduction to Data Processing
Big Data Processing Frameworks
What is Traditional Approach?
MapReduce
Hadoop and Big Data
Distributed Storage System
YARN
Hadoop 1.0/Hadoop 2.0
Advantages of Hadoop
Hadoop Ecosystem
Hortonworks Data Platform

Module 14: Tools and Techniques to Analyse Big Data

Apache Hadoop
Microsoft HDInsight
NoSQL
Hive
Sqoop
PolyBase
Big Data in Excel
Presto

Module 15: Developing a Big Data Strategy

Steps to Develop a Big Data Strategy

Module 16: Implementing Big Data Solution

Steps for Implementing a Big Data Solution

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Who should attend this Hadoop Big Data Certification Course?

This Hadoop Big Data Certification Course is suitable for a wide range of individuals who are interested in mastering the concepts and techniques related to Hadoop and Big Data. This course can be beneficial for a wide range of professionals, including:

Data Professionals
Software Developers
Database Administrators
System Administrators
IT Professionals
Business Analysts
Project Managers

Prerequisites of the Hadoop Big Data Certification Course

There are no formal prerequisites for this Hadoop Big Data Course.

Hadoop Big Data Certification Training Course Overview

Big Data and Analytics Training has emerged as a critical domain. The Hadoop Big Data Certification Training introduces delegates to the world of Big Data and its relevance in modern business and technology landscapes. With data becoming the lifeblood of organisations, understanding and harnessing Big Data and Analytics is essential.

Proficiency in Big Data and Analytics Courses is essential for professionals such as Data Professionals, Software Developers, and IT Professionals. Mastering Big Data Analytics Courses can open doors to lucrative career opportunities and allow individuals to harness the power of data to make informed decisions.

This intensive 2-day Big Data Analytics Course by The Knowledge Academy, empowers delegates with the knowledge and practical skills necessary to navigate the complex landscape of Big Data. Through hands-on experience and expert guidance, delegates will gain the competence to process, analyse, and extract valuable insights from vast data sets.

Course Objectives

To understand the fundamentals of Big Data and Analytics
To employ Hadoop technology to manage and process large datasets
To perform data analysis and gain insights from Big Data
To explore real-world use cases and applications of Big Data Analytics
To master the art of data visualisation for effective communication
To develop practical problem-solving skills in Big Data scenarios

After completing the Hadoop Big Data Training Course, delegates will receive a certification in Hadoop Big Data Analytics, validating their expertise and enhancing their career prospects in the competitive world of Big Data and Analytics. This certification is a testament to their proficiency in handling and interpreting Big Data, making them valuable assets for the delegate's future.

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What’s included in this Hadoop Big Data Certification Course?

World-Class Training Sessions from Experienced Instructors
Hadoop Big Data Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Hadoop Administration Training Course Outline

Module 1: Fundamentals of Hadoop

Apache Hadoop
Why Use Hadoop?

Module 2: Hadoop Ecosystem

Overview
HDFS
MapReduce
YARN
Common
Spark
Hive
Pig
HBase
Oozie
Sqoop

Module 3: Startup and Admin Commands

Startup Commands
Admin Commands

Module 4: Commissioning and Decommissioning Nodes

Commissioning Nodes
Decommissioning Nodes

Module 5: Configuring a Cluster

Overview
Different Types of Configuration Files Drive Hadoop Configuration
Configuration Specification From a core-site.xml File
Configuration Specification From a mapred-site.xml File

Module 6: Maintaining a Cluster

FileSystem Checks
HDFS Balancer Utility
Add New Nodes to the Cluster
Datanode Volume Failures
Database Backups
HDFS Metadata Backup
Purging Older Log Files

Module 7: Monitoring and Troubleshooting Clusters

Managing Resources
Managing HDFS Cluster
Secondary NameNode Configuration
MapReduce Cluster Management

Module 8: Handling Corrupt and Missing Blocks

Use Hadoops’s fsck Filesystem Checking Utility
Find Out which Files have Corrupt Blocks
Deal with the Corrupt Files

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Who should attend this Hadoop Administration Training Course?

This Hadoop Administration Course is suitable for individuals who aim to develop expertise in managing Hadoop clusters and the associated ecosystem components. This course can be beneficial for a wide range of professionals, including:

System Administrators
IT Professionals
Database Administrators
Network Engineers
Software Engineers
Data Engineers
Technical Managers

Prerequisites of the Hadoop Administration Training Course

There are no formal prerequisites for this Hadoop Administration Training Course. However, a basic understanding of Hadoop and a prior knowledge of large data fields would be beneficial for the delegates.

Hadoop Administration Training Course Overview

The Hadoop Administration Training Course stands as a cornerstone for professionals seeking to harness the power of large-scale data management and processing. The organisations rely heavily on Big Data and Analytics, so mastering Hadoop Administration is paramount. This course equips individuals with the knowledge and skills needed to manage and optimise Hadoop clusters efficiently, ensuring the seamless flow of data.

Proficiency in Hadoop Administration is of utmost importance for professionals engaged in the management of Big Data and Analytics. Data Engineers, System Administrators, and IT professionals responsible for maintaining and scaling data infrastructure should aim to master this subject. Hadoop Administration skills enable them to configure, monitor, and troubleshoot Hadoop clusters, ensuring data availability, reliability, and performance.

This 1-day training by The Knowledge Academy empowers delegates with practical insights and hands-on experience in Hadoop Administration. Delegates will learn essential concepts, best practices, and tools for efficiently managing Hadoop clusters, from installation and configuration to security and performance optimisation. The course combines theory and practical knowledge, ensuring that delegates are well-prepared to tackle the challenges of the Hadoop Administration.

Course Objectives

To understand the fundamentals of Hadoop and its role in Big Data and Analytics
To learn cluster installation, configuration, and maintenance techniques
To implement robust security measures to safeguard data integrity
To optimise Hadoop cluster performance for efficient data processing
To acquire hands-on experience with Hadoop ecosystem components
To develop best practices for data management and storage

After completing this Hadoop Administration Training, delegates will receive a prestigious certification recognised within the realm of Big Data and Analytics. This certification validates their expertise in Hadoop Administration, making them valuable assets to organisations in need of efficient data management.

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What’s included in this Hadoop Administration Training Course?

World-Class Training Sessions from Experienced Instructors
Hadoop Administration Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Big Data Architecture Training Course Outline

Module 1: Introduction to Hadoop Development Framework

What is Hadoop?
Apache Hadoop Framework
Hadoop Clusters

Module 2: Real-Time Processing and Batch Processing

Real-Time Processing
Batch Processing

Module 3: Data Formats and Data Lifecycle

What is Big Data?
Structure and Unstructured Data

Module 4: Data Model Creation

What is Data Model Creation?
Benefits of Creating a Structured Repository of Data
Modelling Methodologies
Partitioning
Metadata

Module 5: Database Interface

What is Database Interface?
Features Database Interface
Components of Hue
Apache Hive

Module 6: Scaling

Steps to Successfully Scaling Big Data

Module 7: Security and Privacy

Security Fabric
What are the Risks Associated with Big Data Technologies?
Principles of Data Privacy

Module 8: Hadoop Clusters

What are Hadoop Clusters?
Benefits of Building Clusters
Disadvantages of Hadoop Clusters

Module 9: Selecting Right Technology

Analytic Approach and Data Accuracy
Features and Tracking Types
Customer Service and Support
Data Storage Options Available
Legal Compliance

Module 10: Big Data and Hadoop Administration

Hadoop Administrator(s)
Performance Tuning

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Who should attend this Big Data Architecture Training Course?

This Big Data Architecture Course is designed for professionals and individuals seeking to enhance their understanding and expertise in the field of Big Data architecture. This course can be beneficial for a wide range of professionals, including:

Data Architects
Data Engineers
Database Administrators
IT Managers
Software Developers
Data Scientists
Business Analysts

Prerequisites of the Big Data Architecture Training Course

There are no formal prerequisites for this Big Data Architecture Training Course. However, prior knowledge of Database Management Systems and technologies would be beneficial for delegates.

Big Data Architecture Training Course Overview

Explore the complexities of managing large-scale data with our Big Data Architecture Training Course. This one-day programme provides a thorough understanding of the architecture behind big data systems, covering essential concepts and technologies used to handle and analyse massive datasets effectively.

Ideal for Data Engineers, Architects, Analysts, And IT Professionals, this course is designed for those aiming to enhance their expertise in big data technologies and architecture strategies. It will benefit anyone involved in building or maintaining data infrastructures.

This Knowledge Academy’s 1-day course on Big Data Architecture empowers delegates with the knowledge and practical insights needed to excel in the data-driven world. Delegates will gain a strong understanding of data processing, storage, and analysis techniques, enabling them to drive business success through data-driven strategies.

Course Objectives

To grasp the principles of big data architecture
To understand different big data technologies and frameworks
To design scalable data processing systems
To implement data storage and retrieval solutions
To optimise performance and data integration
To manage data security and governance

After completing the course, delegates will be equipped with the knowledge to design and manage effective big data systems, enhancing their capability to handle large-scale data and drive business insights.

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What’s included in this Big Data Architecture Training Course?

World-Class Training Sessions from Experienced Instructors
Big Data Architecture Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Big Data and Hadoop Solutions Architect Training Course Outline

Module 1: Getting Started with Big Data and Hadoop

Apache Hadoop Ecosystem
Big Data and its Challenges
What is Big Data?
Facebook, Twitter, and Instagram
Types of Data
Data Volume is Growing Exponentially
Hidden Treasure
Characteristics of Big Data
- Scale (Volume)
- Complexity (Varity)
- Complexity (Velocity)
Big Data
3V’s
Some Make it 4V’s
Transactions, Interactions, Observations
Some Make it 4V’s – Characteristics of Big Data 4V’s
Some Make it 4V’s – The V’s of Big Data
Harnessing
Who’s Generating Big Data?
Model has Changed
What’s Driving Big Data?
Types of Big Data
Value of Big Data Analytics

Module 2: What Technology do we have for Big Data?

Big Data Technology
Big Data Architecture
Big Data Design
Big Data Usage Sector
Big Data: Sample Usage – Customer Sentiment
Big Data: Big Data Solutions – A Case Study
Technology Trends
Industries Who Use Big Data?
Case Study

Module 3: Apache Hadoop

What is Apache Hadoop?
Why Use Hadoop?
Hadoop and its Characteristics

Module 4: Hadoop Core Components

Hadoop Ecosystem
Hadoop Services
Different Hadoop Methods
Hadoop Deployment Modes
Motivations for Hadoop
Blocks
Computer Racks and Block Replication

Module 5: Processing Distributed Data

What is HDFS?
Hadoop Distributed File System
Why DFS?
Data Replication
What is Distributed Files System?
Revisit Hadoop Components
What is HSFS?
Goals of HDFS
Features of HDFS
Design of HDFS
Areas Where HDFS is not a Good Fit Today
Abstracting Blocks in HDFS
Benefits of Abstracting Blocks in HDFS
HDFS Components
Main Components of HDFS
Secondary NameNode
NameNode MetaData
DataNodes
Block Placement

Module 6: Jobs Tracker and Task Tracker

Hadoop Distributed File System Architecture
Anatomy of File Write
Anatomy of File Read
Job Tracker
HDFS Creates a New File
HDFS
- Rack Awareness
- Terminal Commands
- Running the Teragen Examples
- Checking the Output
Deployment Modes
MAPRED-SITE.XML

Module 7: Anatomy of a Cluster

Typical Architecture of Hadoop
Hadoop Cluster Architecture
Core Components of Hadoop Cluster
Typical Workflow in HDFS
Hadoop Limitations
Next-Generation Data Architecture
Big Data Refinery Fuels Next-Generation Data Architecture- A Case Study

Module 8: NoSQL

What is NoSQL?
NoSQL Vs RDBMS
Advantages and Disadvantages
ACID Vs BASE
Single CPU RDBMS
NoSQL Data Architecture
Key-Value Stores
Data Model – Column Families
C Graph Database
API Model
Using NoSQL to Manage Big Data

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Who should attend this Big Data and Hadoop Solutions Architect Course?

The Big Data and Hadoop Solutions Architect Course is designed for individuals who are seeking to enhance their expertise in the field of Big Data and Hadoop, with a focus on Solutions Architecture. This Big Data and Analytics Course can be beneficial for a wide range of professionals. Including:

Data Architects
Big Data Engineers
Data Scientists
Database Administrators
IT Managers
Software Architects
System Administrators

Prerequisites of the Big Data and Hadoop Solutions Architect Course

There are no formal prerequisites for this Big Data and Hadoop Solutions Architect Course. However, some prior knowledge of Hadoop would be beneficial for the delegates.

Big Data and Hadoop Solutions Architect Training Course Overview

The Big Data and Hadoop Solutions Architect Course provides essential insights into processing vast datasets. Understanding this training is crucial for professionals aiming to stay ahead in a competitive market where data-driven decisions steer success. This training addresses the heart of modern data challenges, offering a comprehensive understanding of Big Data Analytics Courses.

Proficiency in this course is indispensable for professionals seeking to navigate the complexities of modern data ecosystems. Data scientists, Analysts, and IT Professionals must master this field to leverage the power of data effectively. Learning this training empowers you to harness the full potential of data analytics tools, making you an invaluable asset in any data-driven organisation.

This intensive 1-day training course equips delegates with hands-on experience in Big Data and Analytics. Through practical exercises and real-world scenarios, participants gain proficiency in Big Data and Analytics solutions. By the end of the course, delegates will possess the skills to architect Hadoop solutions, ensuring efficient data processing and analysis.

Course Objectives

To understand the fundamentals of Big Data and Analytics
To develop skills in real-time data processing and storage
To learn advanced data analytics techniques for actionable insights
To explore tools and technologies in the Big Data ecosystem
To understand security and data governance in Big Data environments
To implement best practices for scalable and efficient data solutions

After completing this Big Data and Analytics Course, delegates will receive a prestigious certification. This certification validates your expertise in these courses, making you a recognised authority in the field.

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What’s included in this Big Data and Hadoop Solutions Architect Course?

World-Class Training Sessions from Experienced Instructors
Big Data and Hadoop Solutions Architect Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Big Data Analysis Course Outline

Module 1: Understanding the Fundamentals of Big Data

Understanding the Fundamentals of Big Data
What is Big Data?
Sources of Big Data

Module 2: Big Data Analysis Lifecycle

Big Data Analysis Lifecycle

Module 3: Planning a Big Data Approach

Bottom-Up and Top-Down Planning
Technologies
Considering Use Cases
Thinking Long-Term
Steps for Planning

Module 4: Implementing a Big Data Approach to Business

Recognising Business Challenges
Finding Appropriate Data Sources
Involving The Business
Choosing What to Use

Module 5: Storing Unstructured Information

Storing Unstructured Information
Apache Hadoop
Microsoft HDInsight
Hive
PolyBase
Sqoop
Presto
Microsoft Excel
No SQL

Module 6: Managing Unstructured Information

Challenges of Unstructured Data
Deciding on a Data Source
Preparing for storage
Choosing Storage Solutions

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Who should attend this Big Data Analysis Course?

The Big Data Analysis Course is designed for individuals who want to acquire a comprehensive understanding of handling and interpreting large volumes of data effectively. This Big Data and Analytics Training Course can be beneficial for a wide range of professionals, including:

Data Analysts
Data Scientists
Business Analysts
IT Professionals
Managers and Executives
Entrepreneurs
Software Engineers

Prerequisites of the Big Data Analysis Course

There are no formal prerequisites for this Big Data Analysis Course.

Big Data Analysis Course Overview

Big Data and Analytics Training is a field that has become pivotal in today's world. This course has revolutionised decision-making processes across industries, making it an indispensable domain. With data-driven insights at the forefront of modern business strategies, mastering Big Data Analytics Courses has never been more relevant.

Proficiency in Big Data Analysis Courses is crucial for professionals across various sectors, including IT, Finance, Marketing, and Healthcare. Whether you are a Data Scientist, a Business Analyst, or an aspiring Entrepreneur, mastering Big Data and Analytics is essential. The ability to extract valuable insights from vast datasets is a skill that can propel your career to new heights.

This intensive 1-day Big Data and Analytics Training will empower delegates with the knowledge and tools required to harness the power of data. From understanding data sources to performing in-depth analyses, this course will equip delegates with practical skills. With hands-on exercises and real-world case studies, delegates will leave with a comprehensive understanding.

Course Objectives

To learn data preprocessing techniques for large datasets
To master statistical analysis methods for drawing meaningful insights
To acquire skills in predictive modelling and machine learning algorithms
To explore data visualisation tools for effective communication of findings
To enhance problem-solving abilities through hands-on exercises
To develop expertise in handling unstructured data sources like social media and sensor data

After completing this course delegates will receive a Big Data and Analytics Certification, validating their knowledge and opening doors to a wide range of opportunities. This certification will serve as a testament to their proficiency and commitment to mastering the Big Data and Analytics domain.

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What’s included in this Big Data Analysis Course?

World-Class Training Sessions from Experienced Instructors
Big Data Analysis Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Apache Kafka Training Course Outline

Module 1: Introduction to Big Data

Big Data
Five V’s
Sources of Big Data

Module 2: Overview of Kafka

Publish/Subscribe Messaging
Enter Kafka
Data Ecosystem

Module 3: Installing Kafka

Installing Java and Zookeeper
Hardware Selection
Kafka Clusters

Module 4: Kafka Producers

Creating a Kafka Producer
Sending Message to Kafka
Configuring Producers
Serializers
Partitions

Module 5: Kafka Consumers

Create Kafka Consumer
Poll Loop
Configuring Consumers
Commits and Offsets
Rebalance Listeners
Deserializers

Module 6: Kafka Internals

Cluster Membership
Controller
Replication
Request Processing

Module 7: Reliable Data Delivery

Reliability Guarantees
Replication
Broker Configuration
Using Producers and Consumers in a Reliable System

Module 8: Building Data Pipelines

Considerations When Building Data Pipelines
Kafka Connect
Running Connect
Connectors and Tasks
Workers
Alternatives to Kafka Connect

Module 9: Cross-Cluster Data Mirroring

Use Cases of Cross-Cluster Mirroring
Multicluster Architectures
Apache Kafka’s MirrorMaker

Module 10: Administering and Monitoring Kafka

Overview
Topic Operations
Consumer Groups
Dynamic Configuration Changes
Partition Management

Module 11: Stream Processing

What is Stream Processing?
Stream Processing Concepts
Stream-Processing Design Patterns
Kafka Streams: Architecture Overview

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Who should attend this Apache Kafka Training Course?

The Apache Kafka Course is designed for a wide range of professionals seeking to enhance their knowledge and skills in working with Apache Kafka. This Apache Kafka Certification Training can benefit a wide range of professionals, including:

Data Analysts
Data Engineers
Software Developers
Database Administrators
IT Managers
Technical Managers
Application Architects

Prerequisites of the Apache Kafka Training Course

There are no formal prerequisites for this Apache Kafka Course. However, prior knowledge of Java programming would be beneficial for a smoother learning experience.

Apache Kafka Training Course Overview

Apache Kafka is a real-time distributed event streaming platform and is a vital component of modern data architectures. It enables organisations to process, analyse, and transport data in a scalable, fault-tolerant manner. The significance of Kafka's cannot be overstated. It's the backbone of real-time data processing, making it essential for businesses striving to stay competitive in an ever-evolving landscape.

Proficiency in Apache Kafka is paramount in the age of big data and real-time analytics. Data engineers, developers, and data architects aiming to master Kafka unlock the potential to design robust, scalable, and fault-tolerant systems. Embracing this Apache Kafka Course empowers professionals to navigate the complexities of modern data integration, making them invaluable assets to their organisations.

This intensive 2-day Apache Kafka Training is designed to provide delegates with hands-on experience in Apache Kafka. Delegates will gain practical skills in setting up Kafka clusters, understanding its architecture, and implementing end-to-end data pipelines. They will learn how to optimise Kafka for their specific use cases and troubleshoot common issues, ensuring their organisations can leverage Kafka's full potential effectively.

Course Objectives

To understand Kafka fundamentals, including topics, partitions, and replication
To master Kafka architecture, exploring producers, consumers, and brokers
To implement fault tolerance and high availability in Kafka clusters
To delve into advanced topics like Kafka Connect and Kafka Streams
To learn best practices for configuration and performance tuning
To explore security mechanisms, ensuring data integrity and privacy
To design real-time data processing pipelines using Kafka
To troubleshoot common issues and optimise Kafka clusters for efficiency

After completing this course, delegates receive a prestigious certification. These Apache Kafka Courses validate their expertise in Kafka's architecture, administration, and application, making them highly sought-after professionals in the competitive tech industry.

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What’s included in this Apache Kafka Training Course?

World-Class Training Sessions from Experienced Instructors
Apache Kafka Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

HBase Training Course Outline

Module 1: Introduction to HBase

What is HBase?
HBase and HDFS
Storage Mechanism in HBase
Column Oriented and Row Oriented
HBase and RDBMS
Applications of HBase
Architecture
Installation

Module 2: Shell and General Commands

HBase Shell
General Commands
- status
- version
- table_help
- whoami
Data Definition Language
Data Manipulation Language
Starting HBase Shell
Admin API

Module 3: HBase Table

Create
Listing
Disabling and Enabling
Describe and Alter
Exists
Drop
Count and Truncate Commands

Module 4: Client API and Data

Class HBase Configuration
Class HTable
Class Put and Get
Class Result
Data
- Create
- Update
- Read
- Delete

Module 5: HBase Scan

Scan Using
- HBase Shell
- Java API

Module 6: Security

grant
revoke
User permission

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Who should attend this HBase Training Course?

The HBase Course is designed to impart skills and knowledge to understand and use HBase, a NoSQL Database, to handle vast amounts of data. This course can be beneficial for a wide range of professionals, including:

Data Engineers
Database Administrators
Software Developers
Data Scientists
System Architects
Business Analysts
Quality Assurance Engineers

Prerequisites of the HBase Training Course

There are no formal prerequisites for this HBase Course. However, a basic understanding of Hadoop Architecture and APIs would be beneficial for delegates.

HBase Training Course Overview

HBase is a NoSQL database, and Impala, an analytic query engine, are crucial components of the Hadoop ecosystem. They enable efficient storage and real-time data processing, making them indispensable for data professionals and organisations. This course is designed to provide a comprehensive understanding of these technologies and their integration, allowing learners to harness their power for data management and analysis.

Proficiency in HBase and Impala is essential for individuals working in the fields of data engineering, data analysis, and data science. Professionals who aim to excel in data storage, retrieval, and analysis need to master HBase and Impala to unlock their full potential. This course empowers database administrators, data engineers, and data analysts to enhance their skills and meet the growing demands of the data industry.

This intensive 1-day training course is designed to provide delegates with hands-on experience in deploying and managing HBase and Impala. Delegates will gain practical knowledge in setting up HBase clusters, importing data, and optimising performance. They will learn how to use Impala for real-time query processing, thus improving their data analysis capabilities.

Course Objectives

To understand the fundamentals of HBase, its architecture, and data modelling
To learn to set up and configure HBase clusters for efficient data storage
To gain expertise in data import and export operations in HBase
To explore the basics of Impala and its integration with HBase for analytics
To perform real-time queries using Impala, enhancing data analysis capabilities
To optimise HBase and Impala for improved performance and scalability

After completing the HBase Training Course with Impala, delegates will receive a certification that validates their expertise in HBase and Impala. This certification is a valuable asset, showcasing your ability to handle big data solutions and make informed data-driven decisions.

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What’s included in this HBase Training Course?

World-Class Training Sessions from Experienced Instructors
HBase Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Apache Solr Training Course Outline

Module 1: Introduction to Apache Lucene

Search Engine Basics
Lucene Overview and Features
Indexing Basics
Inverted Indexing Technique
Schema API
Analysers and Query Types
Writing and Searching Index

Module 2: Exploring Apache Lucene

Querying
Faceting and Highlighting
Analysers and Boosting
Spatial Search

Module 3: What is Apache Solr?

Key Features and Solr Vs Databases
Admin UI Quick Tour
Solr Architecture and Schema
Field Types and Fields

Module 4: Overview of Solr Indexing

Tokenisers and Filters
Indexing and Index Handlers
Nested Documents
Transaction Logs and Commits

Module 5: Searching Using Solr

How to Search Using Solr?
Velocity Search UI
Language Analysis
Sort Parameter and Relevance
Boost Query Parser and Query Syntax and Parsing

Module 6: Advanced Features of Solr

Spell Checking
Collapsing, Expanding, and Clustering
Query Re-Ranking
Suggestions and MoreLikeThis
Pagination
Exporting Results and Real-Time Search and Get
Client API’s

Module 7: Administration and SolrCloud

Managing Solrconfig.xml and solr.xml
Managing Multiple Cores
Plugins and JVM Settings
Spell Checking
Logging and Secure Sockets Layer
SolrCloud

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Who should attend this Apache Solr Training Course?

The Apache Solr Training is a dedicated course that equips Software Developers, Data Analysts, and System Administrators with practical knowledge and skills to implement and maintain Solr-based search solutions. The following are some professionals who can benefit from this course:

Software Developers
Data Analysts
System Administrators
Search Engineers
DevOps Professionals
UI/UX Designers
SEO Specialists

Prerequisites of the Apache Solr Training Course

Learners must have an intermediate understanding of Java, Computer Science, Linux, and Databases.

Apache Solr Training Course Overview

Apache Solr is an open-source enterprise search platform built on top of Apache Lucene, designed for fast, scalable, and powerful search capabilities. For organisations, this training provides the benefit of optimising data retrieval processes, improving operational efficiency, and enhancing user experience through advanced search functionality.

For individuals, the training offers the opportunity to develop expertise in configuring, managing, and utilising Solr to handle complex data search and analysis requirements. For delegates, this training enhances career prospects by equipping them with in-demand skills in enterprise search technologies, making them valuable assets in data-driven industries.

In this course, delegates will gain an in-depth understanding of Apache Solr and its capabilities as a powerful enterprise search platform. They will explore Lucene fundamentals, indexing techniques, and schema management while learning how to configure, query, and optimise search functionalities. The training will also cover advanced features such as faceting, highlighting, spell checking, clustering, and query re-ranking, along with SolrCloud for distributed search and scalability. Delegates will also understand administration tasks including managing multiple cores, plugins, and security settings, enabling them to apply Solr effectively in real-world data-driven environments.

Course Objectives

To understand fundamentals of Lucene and Solr architecture
To configure schemas, fields, and indexing techniques effectively
To apply search queries, faceting, and highlighting features
To explore advanced Solr features including clustering and ranking
To administer Solr instances and manage multiple cores efficiently
To implement SolrCloud for scalability and distributed searching

After attending this training, delegates will be able to configure and manage Apache Solr for enterprise-level search applications with confidence. They will be capable of designing efficient indexing strategies, executing complex queries, and implementing advanced search features to improve data accessibility and user experience. Delegates will also be able to administer Solr environments, handle multiple cores, and deploy SolrCloud for high availability and scalability.

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What’s included in this Apache Solr Training Course?

World-Class Training Sessions from Experienced Instructors
Apache Solr Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Big Data Analytics & Data Science Integration Course Outline

Module 1: Big Data Analytics

Big Data Analytics
Bigdata
State of Practice in Analytics
Main Roles for New Big Data Ecosystem

Module 2: Data Analytics Lifecycle

Phases of Data Analytics Lifecycle
- Discovery
- Data Preparation
- Model Planning
- Model Building
- Communicate Results
- Operationalise

Module 3: Basic Data Analytic Methods Using R

R Programming Language
Evolution of R
Features of R
R Programming Language
Exploratory Data Analysis
Confirmatory Data Analysis
Statistical Methods for Evaluation
Regression
Classification

Module 4: Introduction to Clustering

Applications of Clustering
- Marketing
- Retail
- Medical Science
- Sociology

Module 5: Association Rules

Introduction
Apriori Algorithm
Applications of Association Rules
Validation and Testing

Module 6: Regression

Regression Analysis
Linear Regression
Logistic Regression

Module 7: Classification

Decision Tree
Decision Tree Example
Naïve Bayes

Module 8: Time Series Analysis

Introduction
Syntax

Module 9: Text Analysis

Introduction
Term Frequency – Inverse Document Frequency

Module 10: MapReduce and Hadoop

Big Data: Types of Big Data
Hadoop
Hadoop Architecture
NoSQL

Module 11: In-Database Analytics

In-Database Analytics
SQL Essentials
Advanced SQL

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Who should attend this Big Data Analytics & Data Science Integration Course?

The Big Data Analytics & Data Science Integration Course is designed to integrate the principles of Data Science with the tools and technologies needed to deal with Big Data. This course can benefit a wide range of professionals, including:

Data Scientists
Data Analysts
Software Engineers
Managers
IT Professionals
Entrepreneurs
Business Analysts

Prerequisites of the Big Data Analytics & Data Science Integration Course

There are no formal prerequisites for this Big Data Analytics & Data Science Integration Course.

Big Data Analytics & Data Science Integration Course Overview

The Big Data Analytics & Data Science Integration Course is designed to explore the synergy between big data analytics and data science methodologies, providing a comprehensive understanding of their integration. Participants will delve into the core concepts, tools, and techniques essential for extracting meaningful insights from vast datasets.

Proficiency in big data analytics and data science is paramount for professionals aiming to drive data-driven decision-making within their organisations. Data scientists, analysts, IT professionals, and business strategists should aspire to master this domain. This course is tailored for individuals seeking to elevate their skills in the realm of data analysis and interpretation.

This intensive 2-days training offers delegates a unique opportunity to bridge the gap between big data analytics and data science. Through hands-on workshops, real-world case studies, and interactive sessions, participants will acquire practical skills in data preprocessing, predictive modelling, and data visualisation. By the end of the training, delegates will be proficient in leveraging cutting-edge tools and frameworks, empowering them to extract actionable insights from complex datasets.

Course Objectives

To understand the fundamentals of big data analytics and data science integration
To master data preprocessing techniques, including data cleaning and transformation
To explore advanced predictive modelling algorithms for accurate data analysis
To learn to extract insights from unstructured data using natural language processing
To understand the ethical considerations and challenges in big data analytics

Upon completing the Big Data Analytics & Data Science Integration Course, delegates will gain a comprehensive understanding of the synergy between data analytics and data science, allowing them to effectively bridge the gap between these two disciplines. This knowledge will empower them to tackle complex data-related challenges, make data-driven decisions, and excel in their roles in the ever-evolving field of data analysis and science.

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What’s included in this Big Data Analytics & Data Science Integration Course?

World-Class Training Sessions from Experienced Instructors
Big Data Analytics & Data Science Integration Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

ELK Stack Training Outline

Module 1: Introduction to ELK Stack

What is ELK Stack?
ELK Stack Architecture
Importance of ELK
Kibana
ELK Vs Splunk
Advantages and Disadvantages of ELK Stack

Module 2: Installing ELK

Environment Specifications
Java and Elasticsearch Installation

Module 3: Elasticsearch

Basic of Elasticsearch
Elasticsearch Queries
REST API
Plugins

Module 4: Logstash

Configuration
Pitfalls
Logstash Plugins

Module 5: Kibana

Kibana Searches
Visualisations
Dashboards
Kibana Elasticsearch Index

Module 6: Beats

Introduction to Beats
Configuration         
Modules

Module 7: ELK in Production

What is ELK Production?
Monitor Logstash/Elasticsearch Exceptions
Security
Maintainability
Upgrades
Use Cases

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Who should attend this ELK Stack Training Course?

The ELK Stack Course is designed for individuals who aim to enhance their proficiency in working with the ELK Stack, which consists of Elasticsearch, Logstash, and Kibana. This course can benefit a wide range of professionals, including:

Developers
Data Analysts
Security Analysts
Business Analysts
Technical Managers
Database Administrators
Quality Assurance Professionals

Prerequisites of the ELK Stack Training Course

There are no formal prerequisites for this ELK Stack Course. However, basic knowledge of JSON Data Format, SQL and Restful API would be beneficial for delegates.

ELK Stack Training Course Overview

The ELK Stack Training Course is designed to provide a comprehensive understanding of Elastic Stack, a powerful set of tools for data collection, search, and visualisation. Businesses and organisations rely on ELK Stack to efficiently manage and analyse vast amounts of data. Understanding this technology is of paramount importance as it forms the backbone of modern data analytics and operational monitoring.

Proficiency in ELK Stack is crucial for IT professionals, Data Analysts, System Administrators, and DevOps Engineers who seek to harness the power of log analysis, real-time monitoring, and data visualisation. With its applications in troubleshooting, security monitoring, and performance optimisation, mastering ELK Stack is a career-enhancing move for those seeking to stay competitive in the IT landscape.

This intensive 1-day training equips delegates with the skills to deploy, configure, and maintain ELK Stack. Delegates will gain hands-on experience in setting up data pipelines, creating visualisations, and utilising Elasticsearch, Logstash, and Kibana effectively. By the end of the training, delegates will be well-prepared to tackle real-world data challenges, enhancing their problem-solving abilities and job prospects.

Course Objectives

To understand the core components of ELK Stack, including Elasticsearch, Logstash, and Kibana
To learn how to collect, parse, and index data for real-time search and analysis
To create custom dashboards and visualisations for monitoring and reporting
To troubleshoot common issues and optimise ELK Stack for performance
To secure ELK Stack deployments and manage access control
To utilise ELK Stack for log analysis, system monitoring, and security operations

Upon successfully finishing the ELK Stack Training Course, delegates will have acquired the skills necessary for proficiently deploying and managing ELK Stack. This knowledge will enable them to effectively utilise ELK Stack for data analytics, system monitoring, and security operations, making them valuable assets in their professional endeavors.

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What’s included in this ELK Stack Training Course?

World-Class Training Sessions from Experienced Instructors
ELK Stack Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Apache ORC Training Course Outline

Module 1: Introduction to Apache ORC

What is Apache ORC?
ORC Adapters
ORC Types
Level of Indexes
ACID Support

Module 2: Building ORC

Building
- Both C++ and Java
- Java
- C++
- Specify Third-Party Libraries for C++ Build

Module 3: Using in Spark

Spark DDL
Spark Configuration

Module 4: Using in Python

PyArrow
Dask

Module 5: Using in Hive

Hive DDL
Hive Configuration
- Table Properties
- Configuration Properties

Module 6: Using in MapReduce

Reading ORC Files
Writing ORC Files
Sending OrcStruct, OrcList, OrcMap, or OrcUnion through the Shuffle

Module 7: Using ORC Core

Core Java
Core C++

Module 8: Apache ORC Tools

C++ Tools
- orc-contents
- orc-metadata
- csv-import
- orc-scan
- orc-statistics
Java Tools
- Java Meta
- Java Data and Scan
- Java Convert
- Java JSON Schema

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Who should attend this Apache ORC Training Course?

The Apache Optimised Row Columnar (ORC) Training is a specialised course aimed to provide Engineers, Architects, and Developers with an in-depth understanding of high-performance columnar storage format used in the Hadoop ecosystem. The following are some professionals who can benefit from this course:

Data Engineers
Big Data Developers
Database Administrators
Data Scientists
Hadoop Administrators
Cloud Engineers
ETL Developers

Prerequisites of the Apache ORC Training Course

There are no formal prerequisites for this Apache ORC Training Course. However, a basic understanding of Hadoop would be useful.

Apache ORC Training Course Overview

Apache is a non-profit organisation that helps those open-source software projects that are released under the license of Apache. Apache ORC is a self-describing columnar file format enabling efficient querying and storage of data on Hadoop. It uses multi-version concurrency control for supporting ACID transactions. This Apache ORC Training is designed to equip delegates with a detailed knowledge of Apache ORC.

The Knowledge Academy’s Apache OCR Training will introduce delegates to ORC adapters and types. Delegates will gain knowledge of Apache ORC’s three levels of indexes. In addition, delegates will learn how to build Apache ORC. Delegates will get familiarised with hive DDL and configuration, including table and configuration properties.

During this 1-day course, delegates will learn how to read and write ORC files. Delegates will get an understanding of how to send OrcStruct, OrcList, OrcMap through the shuffle. This Apache ORC Training will fully prepare delegates on how to use Apache ORC tools – C++ and Java tools. Post completion of this training, delegates will be able to use Java meta, data, scan, convert, and JSON Schema.

Course Objectives

To understand fundamentals of Apache ORC and its architectur
To apply ORC with Python frameworks like PyArrow and Dask
To implement ORC in MapReduce for file processing
To utilise ORC Core in both Java and C++ environments
To explore ORC tools for scanning and data management
To optimise big data operations with ORC integration

After attending this training, delegates will be able to confidently apply Apache ORC in big data environments to enhance storage efficiency, query performance, and system scalability. They will be capable of building and integrating ORC with multiple platforms, including Spark, Hive, MapReduce, and Python, to streamline data workflows. Delegates will also be skilled in using ORC tools to inspect metadata, manage content, and convert formats for improved data handling.

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What’s included in this Apache ORC Training Course?

World-Class Training Sessions from Experienced Instructors
Apache ORC Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Mastering Apache Ambari Training Course Outline

Module 1: Introducing Ambari Administration

Understanding Ambari Terminology
Using the Administrator Role in Ambari Web
Setting up Ambari to Use an Internet Proxy Server
Managing Cluster Roles
Managing Versions
Managing Local Users
Managing Local Group Membership
Installing Ambari Agents Manually
Understanding Service Users and Groups
Understanding Custom and Private Host Names
Moving the Ambari Server
Configuring LZO Compression
Using LZO Compression with Hive Queries
Using an Existing or Installing a Default Database
Configuring Network Port Numbers
Tuning Ambari Performance
Customising Ambari Log and pid Directories
Managing Host Participation for HDFS and YARN

Module 2: Managing and Monitoring Your Hadoop Cluster

Introducing Ambari Operations
Working with the Cluster Dashboard
Modifying the Cluster Dashboard
Managing Hosts
Establishing Rack Awareness
Managing Services
Managing Service Configuration Settings
Managing Service Configuration Versions
Managing HDFS
Start Kerberos Wizard from Ambari Web
Configuring Log Settings
Managing Host Configuration Groups
Managing Alerts and Notifications
Predefined Alerts

Module 3: Managing High Availability of Services

Managing High Availability
- Enabling AMS
- Configuring NameNode
- Configuring ResourceManager
- Configuring HBase Setting Up Multiple HBase Masters Manually
- Configuring Hive
- Configuring Storm
- Configuring Oozie
- Configuring Atlas
- Enabling Ranger admin

Module 4: Using Ambari Core Services

Using Ambari Core Services
- Understanding Ambari Metrics System
- Grafana Dashboards Reference
- Tuning Performance for AMS
- Setting up AMS Security
- Understanding Ambari log Search
- Understanding Ambari Infra
- Tuning Performance for Ambari Infra

Module 5: Administering Ambari Views

Understanding Ambari Views
Ambari Views Terminology
Increase Memory Available to Ambari Views Server
Review the Number of Expected Concurrent Ambari Views Users
Configure a Trust Store for the Ambari Views Server
Increase Timeout Value for Ambari Views Server
Run a Remote, Standalone Ambari Views Server
Comparing Standalone and Operational Ambari Server Set Up
Running Standalone Ambari Views Servers behind a Reverse Proxy
Prepare to Set Up a Remote, Standalone Ambari Views Server
Configuring Ambari View Instances
Create an Ambari View Instance
Migrate Ambari View Instance Data
Create an Ambari View URL
Set Ambari View Permissions
Configure Ambari Views for Kerberos

Module 6: Configuring Ambari Views

Configuring Specific Views
Configuring Capacity Scheduler View
Configure Your Cluster for Files View
Create and Configure a Files View Instance
Set Up Kerberos for Files View
Configure Local Option for Files View
Configure Custom Option for Files View
Configuring Pig View
Configuring SmartSense View
Configure Workflow Manager View

Module 7: Using an Ambari View

Using YARN Queue Manager View
Using Files View
Using SmartSense View
Using Workflow Manager View

Module 8: Workflow Management

Workflow Manager Basics
Content Roadmap for Workflow Manager
Designing Workflows Using the Design Component
Monitoring Jobs Using the Dashboard
Sample ETL Use Case
Workflow Parameters
Settings Menu Parameters
Job States
Workflow Manager Files

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Who should attend this Mastering Apache Ambari Training Course?

The Mastering Apache Ambari Course is designed for professionals who aim to become proficient in managing and monitoring Hadoop clusters using Apache Ambari. This course can be beneficial for a wide range of professionals, including:

System Administrators
Big Data Engineers
Data Architects
DevOps Engineers
Hadoop Administrators
Project Managers
Security Officers

Prerequisites of the Mastering Apache Ambari Training Course

There are no formal prerequisites for attending this Mastering Apache Ambari Training Course.However, a basic knowledge of Management Tools Architecture, General Relational Databases, Hadoop, and basic UNIX would be beneficial for delegates.

Mastering Apache Ambari Training Course Overview

Apache Ambari is a crucial tool in the field of Big Data management and administration. With the exponential growth of data, organisations are increasingly relying on tools like Ambari to manage, monitor, and maintain their Hadoop clusters efficiently. This course offers an in-depth understanding of Apache Ambari and its relevance in modern data management and analytics.

Proficiency in Apache Ambari is vital for IT professionals, system administrators, and data engineers working with Big Data technologies. It's essential to master Apache Ambari because it simplifies cluster provisioning, monitoring, and management, leading to increased operational efficiency and reduced downtime.

This intensive 2-day training is designed to provide delegates with a comprehensive understanding of Apache Ambari. Delegates will learn how to install, configure, and manage Hadoop clusters effectively using Ambari. Delegates can expect hands-on experience, real-world scenarios, and best practices, ensuring that they are well-prepared to tackle the challenges of managing Big Data infrastructure.

Course Objectives

To understand the fundamentals of Apache Ambari and its role in Big Data management
To gain proficiency in cluster installation and configuration using Ambari
To develop troubleshooting skills to minimise cluster downtime and issues
To implement security measures and user authentication in Hadoop clusters
To get hands-on experience with real-world use cases and scenarios
To acquire the knowledge and skills needed to excel in data infrastructure and administration roles

After completing the course, delegates will receive a certification in Mastering Apache Ambari. This certification is an asset, recognised in the industry, and can enhance your career prospects. It signifies your proficiency in Apache Ambari, making you a sought-after professional in the field of Big Data management and administration.

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What’s included in this Mastering Apache Ambari Training Course?

World-Class Training Sessions from Experienced Instructors
Mastering Apache Ambari Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

High-Dimensional Data Analysis Masterclass Course Outline

Module 1: Introduction to High-Dimensional Data Analysis

Defining High-Dimensional Data
Why High-Dimensional Data Analysis Matters?
Challenges and Complexities in High-Dimensional Data
Real-World Applications and Examples
Ethical Considerations in Data Analysis

Module 2: Data Preprocessing and Dimension Reduction

Data Cleaning and Quality Assurance
Feature Selection Vs Feature Extraction
Principal Component Analysis
t-Distributed Stochastic Neighbour Embedding (t-SNE)
Handling Missing Data in High Dimensions

Module 3: Exploratory Data Analysis (EDA)

Visualising High-Dimensional Data
Scatterplot Matrices and Heatmaps
Clustering Techniques
Dimensionality Reduction for EDA
Identifying Outliers and Anomalies

Module 4: Machine Learning for High-Dimensional Data

Supervised Vs Unsupervised Learning
Classification and Regression in High Dimensions
Ensemble Learning Approaches
Regularisation Techniques
Handling Imbalanced Data

Module 5: Feature Engineering and Selection Strategies

Domain Knowledge and Feature Engineering
Recursive Feature Elimination
Feature Importance Analysis
Dealing with High Correlations
Feature Engineering for Specific Domains

Module 6: Advanced Dimension Reduction Techniques

Non-Negative Matrix Factorization
Independent Component Analysis
Manifold Learning
Autoencoders and Neural Network-based Dimensionality Reduction
Application-Specific Approaches

Module 7: Multivariate Analysis in High Dimensions

Multivariate Regression and Analysis of Variance (ANOVA)
Canonical Correlation Analysis (CCA)
Partial Least Squares (PLS)
MANOVA and Multivariate Classification
Multivariate Time Series Analysis

Module 8: Big Data and High-Dimensional Data Analysis

Challenges of Big Data in High Dimensions
Distributed Computing Frameworks
Scalable Algorithms for High-Dimensional Data
Real-Time Analysis of Streaming High-Dimensional Data

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Who should attend this High-Dimensional Data Analysis Masterclass?

The High-Dimensional Data Analysis Masterclass is tailored for professionals and researchers who work with complex datasets and seek advanced techniques to extract meaningful insights. This course is valuable for a diverse range of individuals, including:

Data Scientists
Machine Learning Engineers
Statisticians
Researchers
Business Analysts
Bioinformaticians
Quantitative Analysts

Prerequisites of the High-Dimensional Data Analysis Masterclass

There are no formal prerequisites for the High-Dimensional Data Analysis Masterclass Course. Participants should ideally have a fundamental understanding of data analysis concepts and some experience working with datasets.

High-Dimensional Data Analysis Masterclass Course Overview

High-Dimensional indicates that the number of dimensions are staggeringly high so that calculations become very complex, and with high dimensional data, the count of features can beat the number of views. High-dimensional statistics concentrate on data sets in which the quantity of components is similar to or larger than the number of observations. Data Analysis systematically applies statistical and logical methods to illustrate, describe, condense, and evaluate data. It can help organisations to evaluate their ad campaigns, better know their customers, personalise content, create content strategies, and improve products.

Every business is using data analytics to increase its performance and improve the bottom line. Therefore, there is high demand for professionals who have in-depth knowledge and skills using Data Analysis. Choosing this course can help you take a wonderful career move, and professionals working in this field can easily demand an impressive salary.

Our 1-day High-Dimensional Data Analysis Masterclass Training course aims to provide delegates with a comprehensive knowledge of data analysis. During this course, delegates will learn about oracle and adaptive compound decision rules for FDR control, classical approaches, statistical methods in eQTL studies, regularised cox regression, combining for adaptation and many more. They will also learn about various regularised methods for the accelerated failure time model and theoretic approach to large-scale multiple testing. Our highly professional trainer with years of experience in teaching such courses will conduct this training course and will help you get a complete understanding of this course.

Course Objectives

To understand the concept and challenges of high-dimensional data
To apply pre-processing and dimension reduction techniques
To conduct exploratory and multivariate data analysis
To implement machine learning strategies for complex datasets
To utilize advanced feature selection and engineering methods
To integrate big data tools for high-dimensional analysis
To develop skills for real-time data analysis applications

After attending this training, delegates will be empowered to apply high-dimensional data analysis techniques confidently and effectively. They will be able to identify, clean, and transform high-dimensional data into a more manageable form, use exploratory data analysis to uncover hidden patterns, and apply various machine learning models to predict and classify complex datasets.

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What’s Included in this High-Dimensional Data Analysis Masterclass?

World-Class Training Sessions from Experienced Instructors
High-Dimensional Data Analysis Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Apache Spark Training Course Outline

Module 1: Introduction to Apache Spark

What is Apache Spark?
Cluster Design
Cluster Management
Performance

Module 2: Apache Spark MLlib

Environment Configuration
Classification with Naive Bayes
Clustering with K-Means
Artificial Neural Networks (ANN)

Module 3: Apache Spark Streaming

Fault Tolerance
Apache Kafka
TCP Stream
Apache Flume

Module 4: Apache Spark SQL

SQL Context
DataFrames
Using SQL
User-Defined Functions
Using Hive

Module 5: Apache Spark GraphX

Environment
Neo4j Browser

Module 6: Spark Databricks

Installing Databricks
Account and Cluster Management
Folders
Jobs and Libraries
Databricks Tables
DbUtils Package

Module 7: Databricks Visualisation

Data Visualisation
REST Interface
Moving Data

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Who should attend this Apache Spark Training Course?

This Apache Spark Training Course is designed for individuals who want to enhance their skills and knowledge in Big Data processing using Apache Spark. This course can benefit a wide range of professionals, including:

Data Scientists
Data Engineers
Software Developers
Database Professionals
Big Data Analysts
Technical Managers
Business Analysts

Prerequisites of the Apache Spark Training Course

There are no formal prerequisites for this Apache Spark Course. However, prior knowledge of Java programming would be beneficial.

Apache Spark Training Course Overview

Apache Spark has emerged as a vital tool for processing and analysing large-scale datasets efficiently. With its widespread use in data engineering and data science, understanding Apache Spark is essential. This course offers a comprehensive exploration of Spark, shedding light on its significance in the modern data landscape enabling professionals to harness its potential for diverse applications.

Proficiency in this course is imperative for professionals across various domains, including data scientists, data engineers, and big data analysts. The ability to work with Spark empowers individuals to handle massive datasets, perform real-time data processing, and derive actionable insights. Mastering Spark is the key to unlocking opportunities and enhancing career prospects in the data and analytics field.

The Knowledge Academy’s 2-day Apache Spark Course equips delegates with the practical skills needed to leverage Apache Spark effectively. During the course, participants will gain hands-on experience in essential Spark components, including Spark SQL, Spark Streaming, and MLlib. They will also learn to build data pipelines, conduct real-time analysis, and optimise Spark applications for enhanced performance.

Course Objectives

To understand the fundamental concepts of Spark and its ecosystem
To gain proficiency in Spark SQL for querying structured data
To learn to process real-time data streams using Spark Streaming
To develop machine learning models with Spark's MLlib library
To create robust data pipelines for scalable data processing
To optimise Spark applications for improved performance
To apply Spark in practical projects to solve real-world problems

Upon completing the Apache Spark Course, delegates will gain a comprehensive understanding of distributed data processing, enabling them to tackle big data challenges with efficiency and confidence. Additionally, they will acquire valuable skills in data analytics, Machine Learning, and real-time data processing, making them highly sought-after professionals in the field of data engineering and data science.

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What’s included in this Apache Spark Training Course?

World-Class Training Sessions from Experienced Instructors
Apache Spark Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Data Integration and Big Data using Talend Course Outline

Module 1: Getting Started with Talend Big Data

Talend Unified Platform Presentation
Knowing About the Hadoop Ecosystem
Running TOSBD for the First Time

Module 2: Building Our First Big Data Job

TOSBD – the Development Environment
HDFS Writer Job

Module 3: Formatting Data

Twitter Sentiment Analysis
Formatting Tweets with Apache Hive

Module 4: Aggregate Data with Apache Pig

Knowing About Pig
Extracting the Top Twitter Users

Module 5: Big Data Architecture and Integration Patterns

Streaming Pattern
Partitioning Pattern

Module 6: Key Concepts in Big Data Storage and Management

Introduction to Big Data Storage Solutions
Importance of Distributed File Systems in Big Data
Data Partitioning, Replication, and Storage Optimization Techniques

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Who should attend this Data Integration and Big Data using Talend Course?

This Data Integration and Big Data using Talend Course is designed for individuals who want to enhance their proficiency in managing and integrating data using the Talend platform. This course can benefit a wide range of professionals, including:

Data Analysts
Data Engineers
IT Professionals
Business Analysts
Database Administrators
Software Developers
Project Managers

Prerequisites of the Data Integration and Big Data using Talend Course

There are no formal prerequisites for this Data Integration and Big Data using Talend Course. However, basic knowledge of Data Warehousing and SQL would be beneficial for delegates.

Data Integration and Big Data using Talend Course Overview

Data Integration and Big Data have become the driving force behind modern businesses, facilitating the seamless management and analysis of massive datasets. In an era where data is the currency of success, understanding how to harness its potential is paramount. This course provides an insightful journey into the world of Data Integration and Big Data using Talend, shedding light on its profound relevance.

Proficiency in Data Integration and Big Data is indispensable for a range of professionals, including data engineers, analysts, business intelligence specialists, and data scientists. Mastery of these subjects empowers individuals to efficiently process, combine, and analyse diverse data sources, enabling data-driven decision-making.

This intensive 1-day training is designed to equip delegates with a comprehensive understanding of Data Integration and Big Data using Talend. Through hands-on exercises and expert guidance, participants will gain practical skills to manage, transform, and extract insights from big data sources efficiently. By the end of the course, delegates will be well-prepared to tackle real-world data integration challenges and harness the power of Big Data in their professional endeavours.

Course Objectives

To master the Talend ETL tool for data extraction, transformation, and loading
To learn to process and integrate data from diverse sources, including structured and unstructured data
To gain proficiency in Big Data concepts and tools like Hadoop and Spark
To develop skills to design and implement data integration solutions in real-world scenarios
To explore best practices for data quality, governance, and security in Big Data projects
To harness the power of Talend for data analytics and visualisation
To collaborate effectively with cross-functional teams in data-related projects

After successfully completing the Data Integration and Big Data using Talend course, delegates will acquire a robust skill set in working with Big Data and enhancing their professional credibility. This knowledge opens doors to exciting career opportunities in data-centric roles and provides a competitive edge, ensuring they stand out in the field.

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What’s included in this Data Integration and Big Data using Talend Course?

World-Class Training Sessions from Experienced Instructors
Data Integration and Big Data using Talend Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Informatica PowerCenter Training Course Outline

Module 1: Introduction to Informatica

Introduction
Use Cases for Informatica

Module 2: Informatica Architecture

Architecture of Informatica
Informatica ETL Tool
Informatica Domain
Node
PowerCenter Repository
Domain Configuration
PowerCenter Client and Server Connectivity
Repository and Integration Service

Module 3: Installing Informatica PowerCenter

Install Oracle
- Database 11g R2
- SQL Developer
Install Informatica
Set Up SQL Developer Domain and Repository
Install Informatica Server and Client

Module 4: Configuring Clients and Repositories

Overview of Informatica Domain
Opening the Administrator Home Page
Creating Repository Services
Configuring Client and Domain
Creating User

Module 5: Source Analyser and Target Designer

Opening a Source Analyser
Importing a Source Table in Source Analyser
Opening a Target Designer and Importing Target in Target Designer
Creating a Folder

Module 6: Mappings

Overview of Mappings
Components of Mapping
Create a Mapping
Mapping Parameters and Variables

Module 7: Workflow and Workflow Monitor

Introduction to Workflow
How to Open Workflow Monitor?
Views in Workflow Monitor

Module 8: Debug Mappings

Introduction
Steps to Use Debugger Mappings

Module 9: Transformations

Introduction to Transformations
Classification of Transformation
Transformation
- Filter
- Source Qualifier and Aggregator
- Router and Joiner
- Rank
- Sequence Generator
- Transaction Control
- Lookup and Re-Usable
- Normaliser
Performance Tuning for Transformation

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Who should attend this Informatica PowerCenter Training Course?

The Informatica PowerCenter Training Course is designed to impart essential skills to work with Informatica PowerCenter, a leading Data Integration tool. The course covers a range of topics helping learners to Extract, Transform, and Load (ETL) data from different sources to a Data Warehouse. This course can be beneficial for a wide range of professionals, including:

Data Integration Specialists
ETL Developers
Database Administrators
Business Intelligence Professionals
Data Architects and Analysts
System Administrators
Solution Architects

Prerequisites of the Informatica PowerCenter Training Course

There are no formal prerequisites for this Informatica PowerCenter Course. However, a basic knowledge of SQL would be beneficial for delegates.

Informatica PowerCenter Training Course Overview

The Informatica PowerCenter Training Course is a comprehensive designed to equip individuals with the essential skills required to harness the power of Informatica PowerCenter. Informatica PowerCenter is the cornerstone of ETL (Extract, Transform, Load) processes, making it highly relevant for anyone involved in data management, analytics, or business intelligence.

Proficiency in this course is crucial for data professionals, ETL developers, data architects, and business intelligence specialists. It empowers professionals to extract, transform, and load data from various sources efficiently, ensuring data accuracy, consistency, and reliability. Organisations highly value individuals who can navigate and optimise the PowerCenter, which is central to maintaining integrity and usability.

This intensive 2-day training will empower delegates with hands-on experience using Informatica PowerCenter. Delegates will learn to create, schedule, and monitor data workflows, enhancing their integration and transformation capabilities. They'll gain insights into best practices, optimising performance, and troubleshooting issues, making them more efficient and effective in their roles.

Course Objectives

To understand the fundamentals of Informatica PowerCenter
To create and manage ETL workflows using PowerCenter
To optimise data integration processes for improved performance
To troubleshoot common issues and errors
To integrate data from various sources, including databases and cloud platforms
To ensure data quality and consistency throughout the ETL process
To develop proficiency in using Informatica PowerCenter's tools and features
To apply best practices in data integration and transformation

After completing the Informatica PowerCenter Training Course, delegates will receive a certification that validates their expertise in using Informatica PowerCenter for data integration and transformation. This certification is a valuable asset, demonstrating their proficiency to employers and colleagues and opening doors to exciting career opportunities.

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What’s included in this Informatica PowerCenter Training Course?

World-Class Training Sessions from Experienced Instructors
Informatica PowerCenter Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Apache Spark and Scala Training Course outline

Module 1: Introduction to Scala

Introduction to Scala and Development of Scala for Big Data Applications
Apache Spark

Module 2: Pattern Matching

Introduction to Pattern Matching
Uses of Scala
Concept of REPL (Read Evaluate Print Loop)
Deep Drive into Scala Pattern Matching
Type Interface and Higher-Order Function
Currying and Traits

Module 3: Executing the Scala Code

Introduction to Scala Interpreter
Creating Static Members with Companion Objects
Implicit Classes in Scala
Different Classes in Scala

Module 4: Classes Concepts in Scala

Understanding the Constructor Overloading
Different Abstract Classes
Hierarchy Types in Scala
Concept of Object Equality and Val and Var Methods in Scala

Module 5: Concepts of Traits with Example

Introduction to Traits in Scala
When to Use Traits?
Linearisation of Traits and the Java Equivalent
Boilerplate Code

Module 6: Scala Java Interoperability and Scala Collection

Implementation of Traits in Scala and Java
Handling of Multiple Traits Extending
Introduction to Scala Collections
Classification of Collections
Difference Between Iterator and Iterable in Scale
List and Sequence in Scala

Module 7: Mutable Collections vs Immutable Collections

Types of Collections in Scala
Lists and Arrays in Scala
List Buffer and Array Buffer
Queue in Scala
Stacks and Sets
Maps and Tuples in Scala

Module 8: Introduction to Spark

What are Spark and Spark Stack?
Ways to Resolve Hadoop Drawbacks
Interactive Operations on Map Reduce
Spark Hadoop YARN
HDFS and YARN Revision
How it is Better Hadoop?
Deploying Spark Without Hadoop
Spark History Server
Cloudera Distribution

Module 9: Spark Basics

Spark Installation
Memory Management
Concept of Resilient Distributed Datasets (RDD)
Functional Programming in Spark

Module 10: Working with RDDs in Spark

Creating RDDs
Operations and Transformation in RDD
RDD Partitioning
FlatMap Method
Scala Map Count
Saveastextfiles
Pair RDD Functions

Module 11: Aggregating Data with Pair RDDs

Introduction to Key-Value Pair in RDDs
How Spark Makes Map-Reduce Operations Faster?

Module 12: Writing and Deploying Spark Applications

Difference Between Spark and Scala
Set and Set Operations
List and Tuple
Concatenating List
Install Apache Maven

Module 13: Parallel Processing

Spark Parallel Processing
Setup Spark Master Code
Introduction to Spark Partitions
Data Locality in Hadoop
Comparing Repartition and Coalesce
Actions of Spark

Module 14: Spark RDD Persistence

Execution Flow in Spark
RDD Persistence Overview
Spark Terminology
Distribution Shared Memory vs RDD
ReduceByKey and SortByKey and AggregateByKey

Module 15: Spark Streaming and Mila

Introduction to Spark Streaming
What is Spark Streaming?
Aspects of Spark Streaming
How does Spark Streaming Work?
Broadcast Variables
Accumulator

Module 16: Spark Variables and RDD Operations

Variables in Spark
Numeric RDD Operations

Module 17: Scheduling or Partitioning

Partitioning in Spark
Hash Partition and Range Partition
Scheduling within and Around Applications
Map Partition with Index
GroupByKey
Spark Master High Availability
Standby Masters with Zookeeper

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Who should attend this Apache Spark and Scala Training Course?

The Apache Spark and Scala Training Course is a specialised that helps professionals to gain expertise in the Big Data Analytics and Distributed Computing sector. This course can be beneficial for a wide range of professionals, including:

Software Developer
Data Scientists
Data Engineers
Business Analysts
Systems Architects
Database Administrators
Data Journalists
Project Managers

Prerequisites of the Apache Spark and Scala Training Course

For attending this Apache Spark and Scala Training Course, a basic knowledge of Java, Database, Query Language, and SQL would be beneficial for delegates.

Apache Spark and Scala Training Course Overview

Apache Spark and Scala have emerged as pivotal tools in the world of Big Data Processing and Analytics. Apache Spark is a robust open-source data processing framework combined with Scala, a high-performance programming language that offers a scalable solution. This course is designed for software developers and IT professionals who can benefit from understanding these technologies to build efficient data processing pipelines.

Proficiency in Apache Spark and Scala is crucial in today's data-driven landscape. It empowers data engineers, data scientists, and analysts to process and analyse large datasets swiftly, enabling data-driven decision-making. For professionals in fields like data science, machine learning, and big data analytics, mastering Spark and Scala is essential.

This intensive 2-day training is designed to provide delegates with a solid foundation in Apache Spark and Scala. Delegates will gain hands-on experience in working with these technologies, learning to develop efficient data processing pipelines, working with distributed datasets, and applying advanced analytics techniques. The course combines theoretical knowledge with practical exercises, ensuring that delegates can immediately apply what they learn in their professional roles.

Course Objectives

To learn how to work with distributed data using Spark RDDs
To explore Spark's DataFrame and Dataset APIs for structured data processing
To master the art of data manipulation, transformation, and analysis with Spark
To develop Spark applications and perform data processing tasks
To discover the integration of Spark with popular data sources and tools
To implement real-world use cases and best practices for Spark and Scala

Upon completing this course, delegates will benefit from a solid foundation in Apache Spark and Scala. They will possess the practical skills and knowledge required to handle and analyse big data effectively, enabling them to excel in their data analytics roles. This course is a valuable investment in their professional development and opens doors to various opportunities in the world of big data analytics.

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What’s included in this Apache Spark and Scala Training Course?

World-Class Training Sessions from Experienced Instructors
Apache Spark and Scala Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Splunk Training Course Outline

Module 1: Splunk Overview

Introduction to Splunk
Installing Splunk
Adding Data in Splunk

Module 2: Splunk Search Processing Language

Pipe Operator
Understanding Basic SPL
Sorting Results
Filtering, Modifying, and Adding Fields
Grouping Results

Module 3: Macros, Field Extraction, and Field Aliases

Field Extraction in Splunk
Field Aliases in Splunk
Splunk Search Query

Module 4: Tags, Lookups, and Correlating Events

Lookups
Tags
Reporting
Alerts

Module 5: Data Models, Pivot, and CIM

Understanding Data Models and Pivot
Common Information Model in Splunk

Module 6: Knowledge Managers and Dashboards in Splunk

Role of a Knowledge Manager
Dashboards
Dynamic Form-Based Dashboards

Module 7: Machine Data Using Splunk Forwarder and Clustering

Splunk Universal Forwarder
Splunk’s Light and Heavy Forwarders
Forwarder Management
Indexer Clusters
Lightweight Directory Access Protocol (LDAP)
Security Assertion Markup Language (SAML)

Module 8: Advanced Data Input in Splunk

Compress the Data Feed
Indexer Acknowledgment
Securing the Feed
Queue Size
Input
Monitor

Module 9: Splunk’s Advanced .conf File and Diag

Understanding Splunk .conf Files
Setting Fine-Tuning Input
Anonymising the Data
Understanding Merging Logic in Splunk

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Who should attend this Splunk Training Course?

Splunk is a leading software platform used for searching, monitoring, and analysing machine-generated Big Data. A Splunk Training Course would be beneficial to those seeking to harness this tool's capabilities for data analysis, visualisation, and operations intelligence. This course can be beneficial for a wide range of professionals, including:

IT Operations Professionals
Security Professionals
Data Analysts
Application Developers
System Administrators
Network Administrators
Database Administrators
Audit and Compliance Officers

Prerequisites of the Splunk Training Course

There are no formal prerequisites for attending this Splunk Training Course. However, a prior understanding of storing and retrieving data would be highly beneficial.

Splunk Training Course Overview

Splunk is a powerful data analytics and visualisation platform that has emerged as a crucial tool in this regard. Splunk Certifications Training Course offers comprehensive insights into Splunk's capabilities, providing a solid foundation for data professionals. Its relevance lies in enabling organisations to extract actionable insights from data, enhance security, and optimise IT operations.

Proficiency in Splunk is crucial because it equips professionals with the skills needed to manage and analyse data, and to make informed decisions efficiently. IT Administrators, Security Analysts, Data Engineers, and Business Intelligence Experts can benefit significantly from mastering Splunk. For IT professionals, it enhances troubleshooting and performance optimisation, while security experts can fortify their defences.

This intensive 2-day training by The Knowledge Academy is designed to provide a fast track to Splunk mastery. Delegates will acquire practical skills in data ingestion, visualisation, and advanced search techniques. They will learn to create dashboards, alerts, and reports, enhancing their ability to turn data into actionable insights. Additionally, participants will delve into Splunk's security and compliance features.

Course Objectives

To install Splunk on different platforms like macOS and Windows
To learn about relative-search and real-time search time modifiers
To acquire an understanding of filtering and reporting commands
To execute a chain of search commands using the pipe operator
To understand the use of data models and pivot in Splunk
To get familiar with the privileges that a user has within Splunk

After attending this training course, delegates will be able to create data models and recognise the patterns of product sales requests. They will also be able to enhance the GUI and real-time visibility in a dashboard to deliver the most up-to-date data on a wide range of performance metrics.

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What’s included in this Splunk Training Course?

World-Class Training Sessions from Experienced Instructors
Splunk Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Couchbase Training Course Outline

Module 1: Introduction to Couchbase Server

What is Couchbase Server?

Module 2: Installing Couchbase Server

Steps to Install Couchbase Server
Estimate Cluster Size Requirements
Network Ports

Module 3: Couchbase Administration Console Basics

Clusters, Buckets, and Servers
Create and Edit Data Buckets
Couchbase Server Statistics

Module 4: Developing with Couchbase

Deployment Options
Basic Operations
Storing Data
Client Interaction with the Cluster

Module 5: Cluster Monitoring

Monitoring Nodes and Buckets
Monitoring Server Nodes

Module 6: Managing Cluster

Adding Node
Removing Node
Rebalancing
Failover with Couchbase
Backup and Restore

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Who should attend this Couchbase Training Course?

This Couchbase Training Course is designed for professionals who want to enhance their skills and understanding of Couchbase, a NoSQL database technology. This course can benefit a wide range of professionals, including:

Developers
Database Administrators
Data Engineers
Software Engineers
System Architects
Technical Leads
IT Professionals

Prerequisites of the Couchbase Training Course

There are no formal prerequisites for this Couchbase Training Course.

Couchbase Training Course Overview

Couchbase is a prominent NoSQL database solution that is widely used in contemporary data-driven industries. Couchbase Training is a comprehensive program that empowers individuals with the expertise to leverage Couchbase. This technology is highly pertinent, facilitating real-time data processing, scalability, and flexibility, proving indispensable for developers, administrators, and data experts.

Proficiency in Couchbase is crucial for various professionals, including database administrators, software developers, and data architects. As data volumes continue to surge, mastering Couchbase ensures that these professionals can efficiently manage, develop, and architect solutions that scale seamlessly and deliver exceptional performance.

This 1-day training offers delegates a unique opportunity to delve deep into Couchbase's capabilities. Delegates will gain practical insights into installation, configuration, and performance optimisation. They will learn to design robust data models, ensuring data consistency and reliability. With hands-on exercises, attendees will be able to apply their knowledge immediately, enhancing their problem-solving skills and productivity.

Course Objectives

To learn how to install and configure Couchbase, ensuring a stable operational environment
To master data modelling to design efficient and scalable database solutions
To develop proficiency in querying and indexing data in Couchbase
To explore advanced topics like data replication and cross-data centre deployments
To optimise performance for high-throughput applications
To gain practical troubleshooting skills for Couchbase-related issues

Upon completion of the Couchbase Training, delegates will gain a comprehensive understanding of Couchbase's NoSQL database system, enabling them to proficiently manage and leverage this technology in their professional roles. This knowledge will empower them to enhance data storage and retrieval processes, improve application performance, and contribute to the success of data-centric projects.

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What’s included in this Couchbase Training Course?

World-Class Training Sessions from Experienced Instructors
Couchbase Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Hadoop Training Course with Impala Outline

Module 1: Introduction to Big Data and Hadoop

What is Big Data?
Characteristics and Challenges of Big Data
Understanding Hadoop as a Big Data Solution
Hadoop Ecosystem Overview

Module 2: Hadoop Distributed File System (HDFS)

Introduction to HDFS
HDFS Architecture
File Storage and Replication
Data Ingestion into HDFS
Managing Data in HDFS

Module 3: Hadoop MapReduce

MapReduce Basics
MapReduce Workflow
Writing and Running MapReduce Jobs
Hadoop Streaming and Custom MapReduce
MapReduce Optimisation Techniques

Module 4: Hive - Data Warehousing and Querying

Introduction to Hive
Hive Data Model and Schema Design
Hive Query Language (HQL)
Managing Tables and Databases in Hive
Hive UDFs and Custom Functions

Module 5: Pig - Data Flow and Scripting

Introduction to Pig
Pig Latin Language
Loading, Transforming, and Storing Data with Pig
Pig UDFs and User-Defined Functions

Module 6: Introduction to Impala

What is Impala?

Impala Vs Hive and Pig
Impala Architecture
Interactive Querying with Impala
Supported Data Formats and Sources

Module 7: Impala Querying Basics

Writing and Running Impala Queries
SQL Syntax in Impala
Query Optimisation in Impala
Working with Tables and Views
Impala Performance Tuning

Module 8: Advanced Impala Optimisation

Impala Data Types and Functions
Complex Queries and Aggregations
Impala Partitioning and Clustering
Impala Security and Access Control
Integrating Impala with Other Hadoop Components

Module 9: Data Ingestion and ETL with Hadoop

Data Ingestion Strategies
ETL (Extract, Transform, Load) Processes with Hadoop
Using Apache Nifi for Data Ingestion
Working with Hadoop's Ecosystem Tools
Real-time Data Processing with Hadoop and Impala

Module 10: Machine Learning with Hadoop and Impala

Introduction to Machine Learning and Data Science
Machine Learning Libraries in Hadoop
Building and Training Machine Learning Models
Deploying Machine Learning Models with Impala

Module 11: Data Visualisation and Reporting

Data Visualisation Tools for Hadoop
Creating Interactive Dashboards
Reporting and Analytics in Hadoop
Integrating Impala with BI Tools
Building Data Reports and Dashboards

Module 12: Hadoop Security and Optimisation

YARN (Yet Another Resource Negotiator)
Resource Management
Hadoop Security
Hadoop Cluster Optimisation
Hadoop High Availability and Disaster Recovery
Hadoop and Cloud Integration

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Who should attend this Hadoop Training Course with Impala?

The Hadoop Training Course with Impala is a specialised course designed to teach the fundamentals of Hadoop and Impala, two powerful tools in the Big Data ecosystem. The following are some of the professionals who can benefit from this course:

Data Analysts
Software Developers
Database Administrators
Business Analysts
System Administrators
Data Scientists
Project Managers
Quality Analysts

Prerequisites of the Hadoop Training Course with Impala

There are no formal prerequisites for this Hadoop Training Course with Impala. However, a basic understanding of data analysis would be beneficial for the delegates.

Hadoop Training Course with Impala Overview

Mastering Hadoop and Impala has become indispensable for professionals aiming to thrive in data-centric industries. This comprehensive training course delves into the heart of Hadoop and Impala technologies, unravelling their significance in handling vast datasets efficiently. With the explosion of data, businesses seek experts who can harness these tools, making this training more pertinent than ever.

Proficiency in Hadoop and Impala is paramount for data engineers, analysts, and IT professionals striving for excellence in data processing. Organisations depend on these technologies to gain actionable insights. Mastering Hadoop and Impala empowers professionals to navigate complex data landscapes, making them indispensable assets for any data-driven enterprise.

This intensive 2-day training equips delegates with hands-on experience in Hadoop and Impala, enhancing their ability to process, analyse, and interpret vast datasets efficiently. Through interactive sessions and real-world examples, delegates learn to optimise queries, troubleshoot issues, and design robust data processing pipelines, ensuring they are well-prepared for the challenges.

Course Objectives

To master the techniques for processing large datasets using Hadoop
To develop advanced skills in Impala for high-speed data querying
To gain hands-on experience in Hadoop ecosystem components like HDFS and MapReduce
To learn to optimise Impala queries for improved performance
To explore data ingestion and storage strategies in Hadoop
To understand security and data governance best practices

After completing this course, delegates receive a certification validating their expertise in Hadoop and Impala technologies. This certification not only demonstrates their proficiency but also opens doors to a plethora of opportunities in data engineering, analytics, and business intelligence.

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What’s included in this Hadoop Training Course with Impala?

World-Class Training Sessions from Experienced Instructors
Hadoop Training Course with Impala Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Apache Maven Training Course Outline

Module 1: Introductions

Build Lifecycle
POM
Profiles
Repositories
Standard Directory Layout
Dependency Mechanism
- Optional Dependencies and Dependency Exclusions

Module 2: Plugins

Plugin Development
Configuring Plug-ins
Plugin Prefix Resolution
Developing Java Plugins

Module 3: Site

Creating a Site
APT Format
Snippet Macro

Module 4: Archetypes

What is an Archetype?
Creating Archetypes

Module 5: Repositories

Installing 3rd party JARs to Local Repository
Deploying 3rd party JARs to Remote Repository
Repository Management
Using Multiple Repositories
Large Scale Centralised Deployments
Mirror Settings
Deployment and Security Settings
Using Proxies
Authenticated HTTPS
Remote repository access through authenticated HTTPS
Relocation

Module 6: Maven Tools and IDE Integration

Maven Auto-Completion Using BASH

Module 7: Maven Community

What is the Maven Community?
Helping with Maven
Guide for New Committers
Testing Development Versions of Plugins
3rd Party Resources

Module 8: Conventions

Maven Conventions
Naming Conventions
When You Can't Use the Conventions

Module 9: The Central Repository

Introduction to the Central Repository
Uploading Artefacts to the Central Repository
Improving the Repository

Module 10: Javadoc API

Maven Artefact
Maven Reporting
Maven Plugin API
Maven Model
Maven Core
Maven Settings

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Who should attend this Apache Maven Training Course?

The Apache Maven Course aims to equip you Software Developers, Build Managers, and Quality Assurance Engineers with the skills to master Maven – a powerful Project Management and Comprehension tool. The following are some professionals who can benefit from this course:

Software Developers
Build and Release Engineers
QA Engineers
DevOps Professionals
Project Managers
Application Support Engineers
System Architects

Prerequisites of the Apache Maven Training Course

There are no formal prerequisites for this Apache Maven Course.

Apache Maven Training Course Overview

Apache Maven is an open-source build automation and project management tool designed primarily for Java projects. For organisations, this training provides the benefit of streamlining software development workflows, improving collaboration, and reducing errors through standardised build and dependency practices. For individuals, the training offers practical knowledge in configuring, managing, and extending Maven to handle complex project requirements with ease.

For delegates, this training enhances career opportunities by equipping them with valuable expertise in one of the most widely adopted tools in modern software development, making them highly sought-after in the industry.

In this course, delegates will gain a strong understanding of Apache Maven as a powerful build automation and project management tool. The training will also cover the use of plugins, archetypes, and conventions that simplify project builds, along with integration into development environments. Delegates will further explore Maven’s central repository, community resources, and advanced configuration options, enabling them to streamline development workflows, manage large-scale projects efficiently, and ensure consistency across software builds.

Course Objectives

To understand Maven lifecycle phases and project structure
To configure POM files and manage dependencies effectively
To manage repositories and deploy artefacts securely and efficiently
To integrate Maven with IDEs and development tools successfully
To follow Maven conventions and apply standardised practices
To utilise Maven community and central repository resources

After attending this training, delegates will be able to confidently apply Apache Maven to manage the complete build lifecycle of software projects. They will be capable of configuring POM files, handling dependencies, and using repositories to manage artefacts effectively. Delegates will also be able to extend Maven functionality through plugins, create reusable archetypes, and integrate Maven seamlessly with development environments.

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What’s included in this Apache Maven Training Course?

World-Class Training Sessions from Experienced Instructors
Apache Maven Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Big Data for Data Engineering Training Course Outline

Module 1: Introduction to Big Data

What is Big Data?
Types of Big Data
Characteristics of Big Data
Advantages of Big Data Processing

Module 2: Introduction to Data Engineering

What is Data Engineering?
What is the Difference Between Data Engineer and Data Scientist?
What Does a Data Engineer Do?
How to Become a Data Engineer?

Module 3: Big Data Platform Blueprint

Ingest
Analyse/Process
Store
Display

Module 4: Developing a Big Data Strategy

Steps to Develop a Big Data Strategy
Steps for Implementing a Big Data Solution

Module 5: ETL Data Pipeline

What is ETL?
NoSQL Stores
Data Warehouse
What are Data Marts?
Types of Data Marts
OLAP and OLAP Cubes

Module 6: File I/O Processing and Regular Expressions

File I/O Processing
Regular Expressions

Module 7: Data Visualisation

Business Intelligence Tools
Identity and Device Management
How to Use Web servers to Display Content?

Module 8: Hadoop

What is Hadoop?
Advantages of Hadoop
Hadoop Ecosystem
Hortonworks Data Platform

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Who should attend this Big Data for Data Engineering Training Course?

The Big Data for Data Engineering Course is specifically tailored for Data Engineers, Software Engineers, and other professionals diving into the vast world of Big Data. The following are the other professionals who can benefit from this course:

Data Engineers
Data Scientists
Software Developers
System Administrators
Business Analysts
DevOps Engineers
Project Managers

Prerequisites of the Big Data for Data Engineering Training Course

There are no formal prerequisites for Big Data for Data Engineering Course, but a basic understanding of Big Data concepts can be beneficial.

Big Data for Data Engineering Training Course Overview

Big data is a set of data that is large in size and still expanding exponentially with time. The utilisation of big data enables businesses to monitor different consumer-related trends and patterns. Big data engineers communicate with huge data by processing databases in wide-large scale computing environments. It provides organisations with analyses that assist them in evaluating performance, predicting future moves and market trends, and recognising market demographics to support business growth. Adding Big Data skills for Data Engineering in learners' profiles will surely help them climb the ladder of success in value and career opportunities.

Our 1-day Big Data for Data Engineering Training course aims to provide delegates with a comprehensive knowledge of big data and data engineering. During this course, delegates will learn about the key difference between data engineers and data scientists. They will also learn about various essential topics such as characteristics of big data, working with data engineers, ETL data pipeline, PostgreSQL DB, business intelligence tools, Hortonworks data platform, and many more. Our highly professional trainer with years of experience in teaching such courses will conduct this training course and will help you get a complete understanding of big data engineering.

This training will also cover the following concepts:

Advantages of big data processing
How to become a data engineer?
Steps to develop a big data strategy
Types of data marts
NoSQL stores
NodeRED
Hadoop ecosystem

Course Objectives

To understand the fundamentals, types, and characteristics of Big Data
To explore the roles and responsibilities of a Data Engineer
To learn the Big Data platform architecture and data flow process
To develop strategies for Big Data implementation and solution design
To gain practical knowledge of ETL pipelines, data warehouses, and data marts
To apply file I/O processing and regular expressions for data manipulation
To visualise and present data using business intelligence and web tools
To understand Hadoop architecture, ecosystem components, and advantages

At the end of this Big Data for Data Engineering Training course, delegates will be able to develop a big data strategy and implement big data solutions properly. They will also be able to use various webservers to display content effectively and efficiently.

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What’s included in this Big Data for Data Engineering Training Course?

World-Class Training Sessions from Experienced Instructors
Big Data for Data Engineering Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

SAS (Statistical Analysis System) Training Course Outline

Module 1: Introduction to SAS

SAS and Its Applications
Key Features and Benefits of SAS
Understanding the SAS Programming Language
Introduction to SAS Components and Modules

Module 2: Data Manipulation with SAS

Importing Data in SAS
Data Cleaning and Validation Techniques

Module 3: Data Management in SAS

Creating and Modifying Variables
Working with Missing Values and Outliers
Subset Selection and Filtering Data
Data Transformation and Recoding Techniques

Module 4: Statistical Analysis with SAS

Descriptive Statistics and Data Summarisation
Hypothesis Testing and Statistical Inference
Analysis of Variance (ANOVA) and Covariance (ANCOVA)

Module 5: Advanced SAS Programming

Advanced Data Manipulation Techniques
Working with Complex Datasets and Structures
Macros and Automation in SAS Programming
Debugging and Error Handling in SAS

Module 6: SAS Graphics and Reporting

Generating Graphical Representations of Data
Creating Custom Reports and Summaries
Data Visualisation Techniques in SAS
Presentation-Quality Output in SAS

Module 7: SAS Procedures and Functions

Exploring SAS Procedures for Specific Analyses
Understanding SAS Functions and Their Applications
Using SAS Libraries
Advanced Data Processing with SAS Procedures

Module 8: SAS Data Mining and Machine Learning

Introduction to Data Mining and Machine Learning
Applying Data Mining Techniques in SAS
Predictive Modelling and Classification
Clustering in SAS

Module 9: SAS for Big Data Analytics

Handling and Analysing Big Data with SAS
Parallel Processing and Optimisation in SAS
Big Data Visualisation and Reporting

Module 10: SAS Integration and Automation

Integrating SAS with Other Systems and Platforms
Automation and Scheduling of SAS Processes
SAS Integration with Databases and Data Warehouses
Web Services and APIs in SAS Integration

Module 11: SAS Deployment and Administration

SAS Server Architecture and Deployment Options
User Management and Security in SAS
Performance Tuning and Optimisation in SAS
Monitoring and Troubleshooting SAS Environments

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Who should attend this SAS (Statistical Analysis System) Training Course?

SAS (Statistical Analysis System) Training Course aims to help professionals acquire hands-on experience in using the SAS environment, learn coding in the SAS language, and understand how to analyse and visualise data effectively. This course can be beneficial for a wide range of professionals, including:

Data Analysts
Business Intelligence Analysts
Quantitative Researchers
Market Research Analysts
Operations Research Analysts
Senior BI Managers
Team Leaders

Prerequisites of the SAS (Statistical Analysis System) Training Course

There are no prerequisites required for attending the SAS (Statistical Analysis System) Training Course. However, prior experience in data analysis and familiarity with basic statistical concepts would be beneficial.

SAS (Statistical Analysis System) Training Course Overview

SAS, the Statistical Analysis System, is a powerful tool in the world of data analytics and statistics. This comprehensive training course introduces participants to the capabilities of SAS and its significance in data-driven decision-making. Whether you're a data scientist, business analyst, or a student aspiring to enter the field, this course equips you with essential skills.

Proficiency in SAS is crucial because it enables professionals to efficiently analyse and interpret data, turning it into actionable insights. Data analysts, statisticians, business analysts, and even healthcare professionals stand to benefit from mastering SAS. This course is ideal for those looking to boost their career prospects and make informed decisions using data.

This intensive 2-day training is designed to fast-track your understanding of SAS. Delegates will learn how to import, clean, and transform data, conduct statistical analyses, and create informative visualisations. Practical hands-on exercises and real-world case studies will empower delegates to apply SAS concepts immediately in their work. By the end of the course, you'll have the skills to efficiently work with data and gain insights that drive decision-making and business success.

Course Objectives

To develop proficiency in SAS programming, including procedures, functions, and macros
To explore predictive modelling, regression analysis, and data mining using SAS
To use SAS reporting tools for effective data interpretation, visualisation, and reporting
To handle large datasets and perform complex statistical computations
To foster a practical understanding of SAS applications in various industries
To provide solid knowledge for further exploration and advanced study of SAS

After attending this training course, delegates will be able to build data-driven decision-making processes utilising SAS tools and capabilities. They will be able to execute data mining on enormous databases and uncover significant patterns and trends. They will also be able to use SAS methodology and approaches to tackle real-world business challenges.

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What’s included in this SAS (Statistical Analysis System) Training Course?

World-Class Training Sessions from Experienced Instructors
SAS (Statistical Analysis System) Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Apache Storm Training Course Outline

Module 1: Introduction to Apache Storm

What is Apache Storm?
Apache Storm Vs Hadoop
Use-Cases of Apache Storm
Apache Storm – Benefits

Module 2: Apache Storm Core Concepts

Topology
Tasks
Workers
Stream Grouping

Module 3: Storm-Cluster Architecture

Architecture of Apache Storm Cloud
Components

Module 4: Apache Storm Workflow

Workflow of Apache Storm
Modes in a Storm Cluster

Module 5: Distributed Messaging System

What is Distributed Messaging System?
Thrift Protocol

Module 6: Apache Storm – Installation

Step 1: Verifying Java Installation
Step 2: ZooKeeper Framework Installation
Step 3: Apache Storm Framework Installation

Module 7: Apache Storm Working Example

Scenario – Mobile Call Log Analyser
Spout Creation
Bolt Creation
Call log Creator Bolt
Call log Counter Bolt
Creating Topology
Local Cluster
Building and Running the Application
Non-JVM languages

Module 8: Apache Storm Trident

Trident Topology
Trident Tuples
Trident Spout
Trident Operations
State Maintenance
Distributed RPC
When to Use Trident?
Working Example of Trident
Building and Running the Application

Module 9: Apache Storm in Twitter

Twitter
Hashtag Reader Bolt
Hashtag Counter Bolt
Submitting a Topology
Building and Running the Application

Module 10: Apache Storm in Yahoo! Finance

Spout Creation
Bolt Creation
Submitting a Topology
Building and Running the Application

Module 11: Apache Storm Applications

Klout
Weather Channel
Telecom Industry

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Who should attend this Apache Storm Training Course?

The Apache Storm Course is designed for individuals who wish to enhance their expertise in real-time data processing and stream processing using the Apache Storm framework. This course can benefit a wide range of professionals, including:

Data Scientists
Data Engineers
Software Developers
Database Administrators
IT Professionals
Technical Managers
System Architects

Prerequisites of the Apache Storm Training Course

There are no formal prerequisites for this Apache Storm Training Course. However, prior knowledge of Java programming would be beneficial.

Apache Storm Training Course Overview

In today's data-driven world, Apache Storm stands out as a powerful open-source stream processing framework. This essential tool empowers organisations to efficiently handle massive data streams, providing real-time insights crucial for informed decision-making. In an age where data holds supreme importance, this course is designed to equip individuals with the expertise needed to unlock Apache Storm's potential, positioning them as invaluable assets in industries driven by data.

Proficiency in Apache Storm is indispensable for professionals working in data engineering, data science, and data analytics. It's equally relevant for software developers and architects seeking to build real-time data processing applications as data becomes the lifeblood of businesses. Those who master Storm gain a competitive edge, enabling them to design efficient, real-time data pipelines and derive actionable insights from incoming streams.

This intensive 1-day training empowers delegates with the expertise needed to harness the full potential of Apache Storm. Delegates will gain hands-on experience in setting up Storm clusters, designing topologies, and processing real-time data efficiently. They'll learn best practices and troubleshooting techniques, ensuring they can contribute effectively to real-time data processing projects from day one.

Course Objectives

To understand the core concepts of Apache Storm and its role in real-time data processing
To learn how to set up and configure Storm clusters for optimal performance
To design and develop Storm topologies for processing real-time data streams
To implement data spouts and bolts to ingest and manipulate data within Storm
To explore real-world use cases and best practices for Storm-based solutions
To ensure fault tolerance and scalability in Storm applications

Upon completing this Apache Storm Training, delegates will gain a deep understanding of stream processing, enabling them to harness real-time data insights for informed decision-making. This expertise will not only enhance their career prospects but also make them valuable contributors in data-driven industries, providing a competitive edge in a rapidly evolving professional landscape.

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What’s included in this Apache Storm Training Course?

World-Class Training Sessions from Experienced Instructors
Apache Storm Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Data Warehousing Training Course Outline

Module 1: Introduction to Data Warehouse

What is Data Warehousing?
Features of Data Warehouse
Types of Data Warehouse
Components of Data Warehouse
Use of Data Warehouse
Advantages of Data Warehouse
Disadvantages of Data Warehouse
Data Warehouse Tools
Data Warehouse Applications
Integrating Heterogeneous Databases

Module 2: Terminologies

Metadata
Metadata Repository
Data Cube
Data Mart
Virtual Warehouse

Module 3: Dimensions and Facts

Facts
Dimensions

Module 4: Modelling

Data Warehouse Modelling Overview

Module 5: Delivery Process

Delivery Method
IT Strategy
Education and Prototyping
Technical Blueprint

Module 6: System Processes

Process Flow in Data Warehouse
Extract and Load Process
Clean and Transform Process
Backup and Archive the Data

Module 7: Data Warehouse Architecture

Three-Tier Data Warehouse Architecture
Data Warehouse Models
Load, Warehouse, and Query Manager

Module 8: Data Warehouse OLAP

Types of OLAP Servers
OLAP Operations
OLAP Vs OLTP

Module 9: Relational and Multidimensional OLAP

Relational OLAP
Multidimensional OLAP

Module 10: Data Warehouse Schemas

Star Schema
Snowflake Schema
Fact Constellation Schema
Schema Definition

Module 11: Horizontal and Vertical Partitioning

Introduction to Partitioning
Horizontal Partitioning
Vertical Partitioning

Module 12: Metadata Concepts

Metadata Categories
Role of Metadata

Module 13: System and Process Managers

System Managers
Process Managers

Module 14: Security and Backup

Security Requirements
User Access
Impact of Security on Design
Hardware and Software Backup

Module 15: Tuning and Testing

Tuning
Testing

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Who should attend this Data Warehousing Training Course?

The Data Warehousing Course is suitable for individuals who wish to enhance their understanding and proficiency in the field of Data Warehousing. This Data Warehousing Course can be beneficial for a wide range of professional, including:

Data Professionals
Business Analysts
IT Managers
Software Developers
Business Intelligence Professionals
Project Managers
Database Professionals

Prerequisites of the Data Warehousing Training Course

There are no formal prerequisites for this Data Warehousing Training Course. However, a basic understanding of basic database concepts would be beneficial for delegates.

Data Warehousing Training Course Overview

Data Warehousing is the process of centralising, cleaning, and transforming data from multiple sources into a unified repository for analytical purposes It plays a pivotal role in collecting, storing, and managing vast amounts of data, enabling organisations to make informed decisions. This Data Warehousing Course provides delegates with a comprehensive understanding of data warehousing, including its significance in data analytics, business intelligence, and decision-making processes.

Proficiency in this Data Warehousing Course is essential for a wide range of professionals, including Data Analysts, Business Intelligence Experts, Database Administrators, and IT Managers. Data Warehousing proficiency empowers individuals to extract valuable insights from large datasets, enhance strategic planning, and make data-driven decisions.

The Knowledge Academy’s 1-day Data Warehousing Course is designed to equip delegates with the knowledge and practical skills needed to excel in data warehousing. Delegates will learn the fundamentals of data warehousing architecture, ETL (Extract, Transform, Load) processes, data modelling, and data warehouse design. Hands-on exercises and real-world case studies will provide a holistic understanding of data warehousing concepts.

Course Objectives

To understand the core concepts and principles of Data Warehousing
To learn how to design and implement an effective Data Warehousing system
To explore data modelling techniques for Data Warehousing
To gain proficiency in ETL processes and tools
To acquire knowledge of data warehouse management and maintenance
To analyse real-world case studies to apply data warehousing principles

After completing the Data Warehousing Course, delegates will receive a certification in Data Warehousing, validating their expertise in this critical field. This Data Warehousing Certification is recognised by industry leaders and will enhance career opportunities, enabling individuals to contribute effectively to their organisations.

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What’s included in this Data Warehousing Training Course?

World-Class Training Sessions from Experienced Instructors
Data Warehousing Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Spark Training for Python Developers Course Outline

Module 1: Set Up a Spark Virtual Environment

Data-Intensive Applications Architecture
Overview of Spark
Introduction to Anaconda
Setting a Spark Powered Environment
Building App with PySpark

Module 2: Building Batch and Streaming Apps with Spark

Architecting Data-Intensive Apps
Analysing the Data
Exploring GitHub

Module 3: Juggling Data with Spark

Serializing and Deserializing Data
Storing and Deleting Data
Processing Data

Module 4: Data Using Spark

Classifying Spark MLlib Algorithms
Spark MLlib Data Types
Reading and Writing Data with Spark
Introduction to Spark Structured Streaming

Module 5: Data Manipulation

Loading and Inspecting Data
Performing Data Transformations
Partitioning and Repartitioning Data
Caching and Persisting Data

Module 6: Visualising Insights and Trends

Pre-process Data for Visualisation
Creating Word Clouds

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Who should attend this Spark Training for Python Developers Course?

The Spark Training for Python Developers Course is a specialised course aimed at Python Developers keen to enhance their skills in Big Data processing using Apache Spark. This course can be beneficial a wide range of professionals, including:

Python Developers
Data Scientists
Machine Learning Engineers
Data Engineers
Data Analysts
DevOps Engineers
Project Managers

Prerequisites of the Spark Training for Python Developers Course

There are no formal prerequisites for this Spark Training for Python Developers Course. However, a basic understanding of SQL and Python programming would be beneficial for delegates.

Spark Training for Python Developers Course Overview

The Spark Training for Python Developers Course provides a comprehensive understanding of Spark and its integration with Python, emphasising its relevance in harnessing big data, conducting efficient data processing, and unlocking powerful analytics and machine learning capabilities. As organisations continue to rely on data-driven decisions, proficiency in Spark for Python developers is a must-have skill.

Proficiency in Apache Spark is crucial for Data Engineers, Data Scientists, and Software Developers who aspire to work with big data, streamline data processing pipelines, and build scalable machine learning models. With the exponential growth of data, the ability to harness Spark's processing power and flexibility becomes paramount for professionals looking to enhance their career prospects in the data industry.

This intensive 2-day training equips delegates with the knowledge and practical skills needed to leverage Apache Spark effectively with Python. Delegates will gain hands-on experience in data manipulation, distributed computing, and creating data pipelines, ensuring they are well-prepared to tackle real-world big data challenges. By the end of the course, delegates will be able to develop Spark applications in Python, optimise data processing tasks, and execute advanced analytics with confidence.

Course Objectives

To gain a solid understanding of Apache Spark and its ecosystem
To develop proficiency in using Python to interact with Spark
To learn to process and analyse big data efficiently
To master the art of creating data pipelines using Spark
To explore machine learning and advanced analytics with Spark and Python
To understand the best practices for optimising Spark applications
To acquire practical knowledge to solve real-world data challenges
To enhance your ability to work with distributed computing frameworks

After completing the course, delegates will receive a certification that validates their skills and knowledge. This certification is a valuable asset for your career, demonstrating your expertise in big data processing and analytics and opening doors to exciting opportunities in the data industry.

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What’s included in this Spark Training for Python Developers Course?

World-Class Training Sessions from Experienced Instructors
Spark Training for Python Developers Certificate
Digital Delegate Pack

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Not sure which course to choose?

Speak to a training expert for advice if you are unsure of what course is right for you. Give us a call on 01344203999 or Enquire.

Skills Gained from Big Data and Analytics Training

Big Data and Analytics Training equip learners with the technical and analytical expertise to interpret complex data, extract insights, and drive informed decisions. It builds strong foundations in data handling, visualisation, and predictive analysis using modern tools. The following are the skills that you will gain:

Data Collection and Processing: Professionals learn to gather, clean, and manage large datasets using advanced data management and ETL tools and techniques.
Statistical and Predictive Analysis: Gain skills in applying statistical models and predictive algorithms to identify patterns and trends.
Data Visualisation: Master tools like Tableau, Power BI, and Python libraries to present data-driven insights effectively.
Machine Learning Fundamentals: Learners understand key Machine Learning concepts for automating decision-making and enhancing business intelligence.
Business Insight and Decision-making: Develop the ability to translate complex analytical findings into actionable business strategies.

Who will Benefit from these Big Data and Analytics Courses?

Who will Benefit from these Big Data and Analytics Courses

Completing the Big Data and Analytics Trainings from The Knowledge Academy enables professionals to pursue roles across technology, finance, marketing, and operations where data-driven decision-making is essential. The key roles you can pursue after completing these courses include:

Data Analyst: Interpret large and complex datasets to uncover patterns, support evidence-based decision-making, and guide strategic business improvements across departments.
Data Engineer: Design, build, and maintain robust data pipelines and architectures that ensure secure and reliable data flow across organisational systems.
Business Intelligence Analyst: Transform raw data into visual dashboards and actionable insights, empowering stakeholders to track performance and make informed business decisions.
Data Scientist: Develop predictive models using machine learning and statistical techniques to forecast trends, automate analysis, and optimise strategic planning.
Analytics Consultant: Advise organisations on effective data strategies, helping them harness analytics tools to enhance productivity, innovation, and long-term business growth.

Package deals for Big Data and Analytics Training

Our training experts have compiled a range of course packages on a variety of categories in Big Data and Analytics Training, to boost your career. The packages consist of the best possible qualifications with Big Data and Analytics Training, and allows you to purchase multiple courses at a discounted rate.

Save Up to 40%3 Courses

Complete Big Data Package

Microsoft Power BI Course £995
Big Data Analysis £1995
Hadoop Administration Training £1995

Total Without Package: £4985

Package Price: £2995 (Save £1990)

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Big Data and Analytics Training FAQs

What is a Big Data Analytics Course?

Big Data Analytics Courses teach data analysis for large datasets, including data mining, machine learning, and statistics, which is used in data-driven decision-making across various fields.

Is Big Data and Analytics a good career?

Yes, Big Data and Analytics is a rapidly growing field with high demand for skilled professionals. It offers competitive salaries, good job security, and the opportunity to work on challenging and interesting projects.

Can a beginner learn Big Data?

Yes, a beginner can learn Big Data. Starting with the fundamentals and gradually building expertise through our online course, tutorials, and practical experience can help beginners become proficient in this field.

How do I start a career in Big Data?

Starting a career in Big Data includes learning the basics of Big Data and choosing a specific role, gaining relevant experience, creating a portfolio, and connecting with people in the Big Data field.

What skills are required for Big Data and Analytics?

To succeed in Big Data and Analytics, you need skills in data analysis, programming (Python, R), knowledge of data tools (Hadoop, Spark), statistics, and domain expertise. Effective communication and problem-solving abilities are also crucial for deriving meaningful insights.

Where can I take a Big Data and Analytics Training Course?

The Knowledge Academy offers Big Data and Analytics Training in a range of locations around the world, making it easy to find a training venue near you. You can also opt for our online instructor-led training sessions or self-paced training mode which allows you to complete the courses according to your timing.

What are the benefits of taking an Online Big Data and Analytics Training Courses?

Online Big Data and Analytics Training Courses offer flexibility, allowing self-paced learning without travel costs. They provide access to expert instructors and diverse resources as well.

Does Big Data require coding?

Yes, Big Data usually requires coding.

What does a Big Data Analyst do?

A Big Data Analyst processes and analyses large datasets to extract insights using programming and statistical tools. Their role is essential for data-driven decision-making and in improving business operations.

Is Big Data Analytics a good course?

Yes, Big Data Analytics is a good course choice because it provides valuable skills, high-demand job opportunities, and the ability to work with large datasets, making it a promising field for future employment.

Is Big Data still in demand?

Yes, Big Data is still in demand across multiple industries due to its valuable insights and decision-making potential. The continuous growth of data generation and its benefits to businesses ensure this demand remains strong.

What is the salary of a Big Data fresher?

The average salary for a Big Data fresher is £34,548 per year. The salary can differ due to various factors like location and experience.

What are the best Big Data and Analytics Training in the United Kingdom?

Please see our Big Data and Analytics Training available in the United Kingdom

Which is the best training institute/provider of Big Data and Analytics Training in the United Kingdom?

The Knowledge Academy is one of the Leading global training provider for Big Data and Analytics Training.

What is the cost/training fees for Big Data and Analytics Training in the United Kingdom

The training fees for Big Data and Analytics Training in the United Kingdom starts from £3495

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"Really good course and well organised. Trainer was great with a sense of humour - his experience allowed a free flowing course, structured to help you gain as much information & relevant experience whilst helping prepare you for the exam"

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