Data Science Courses

Online Instructor-led (3 days)

Classroom (3 days)

Online Self-paced (24 hours)

Python Data Science Training Course Outline

Module 1: Introduction of Python

  • What is Python?
  • What can be Done by Using Python Programming Language?
  • Why Python?

Module 2: Working with IPython

  • Launching IPython Shell and Jupyter Notebook
  • Keyboard Shortcuts in the IPython Shell
  • Special Commands of Python
    • Pasting Code Blocks: %paste and %cpaste
    • Running External Code: %run
    • Timing Code Execution: %timeit
    • %magic and %Ismagic
  • IPython’s In and Out Objects
  • IPython and Shell Commands
  • Errors and Debugging
  • Profiling and Timing Code

Module 3: Introduction to NumPy

  • Understand Data Types in Python
  • NumPy Arrays
  • Universal Functions
  • Aggregations: Min, Max and more
  • Computation on Arrays: Broadcasting
  • Comparison, Boolean Logic, and Masks
  • Fancy Indexing
  • Sorting Arrays
  • NumPy’s Structured Array

Module 4: Working with Pandas

  • Installing and Using Pandas
  • Pandas Objects
  • Data Indexing and Selection
  • Operating on Data in Pandas
  • Handling Missing Data
  • Hierarchical Indexing
  • Concat and Append
  • Merge and Join
  • Aggregations and Grouping
  • Pivot Tables
  • Vectorised String Operations
  • Working with Time Series
  • eval() and query()

Module 5: Visualisation with Matplotlib

  • Overview of Matplotlibs
  • Object Oriented Interface
  • Two Interfaces
  • Simple Line Plots and Scatter Plots
  • Visualising Errors
  • Density and Contour Plots
  • Histograms, Binnings, and Density
  • Customising Plot Legends
  • Customising Colorbars
  • Multiple Subplots
  • Text Annotation
  • Three-Dimensional Plotting in Matplotlib
  • Visualisation with Seaborn

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Who should attend this Python Data Science Training Course?

This Python Data Science Course is designed to instruct Software Developers and Data Scientists in the advanced principles of utilising Python for Data Science Applications. The following professionals can benefit from these Data Science Courses:

  • Software Developers
  • Data Analysts
  • Machine Learning Engineers
  • Product Managers
  • Business Analysts
  • Natural Language Processing Professionals
  • Data Scientists

Prerequisites of the Python Data Science Training Course

There are no formal prerequisites for attending these Python Data Science Course. However, a basic understanding of programming would be beneficial.

Python Data Science Training Course Overview

Python is an open-source programming language known for its premier status and robust capabilities. It is also renowned for its user-friendly nature and extensive libraries tailored for data manipulation and analysis. This versatile language accommodates multiple programming paradigms, including object-oriented, functional, and structured programming.

Understanding Python for Data Science is crucial for professionals aiming to master the subject. Data scientists, analysts, and anyone working with large datasets benefit from the efficiency and flexibility Python offers. Proficiency in this language opens doors to advanced data manipulation techniques, aiding professionals in extracting meaningful insights and patterns from complex datasets.

This 3-day Python Data Science Training by The Knowledge Academy is designed to equip delegates with essential programming skills. Delegates will explore three-dimensional function visualisation, mastering techniques such as histograms, binning, and density estimation. The course dives deep into constructing arrays, working with Python lists and custom arrays, and enhances data manipulation skills using the powerful Pandas library.

Course Objectives

  • To empower delegates with essential Python programming skills for data science
  • To provide in-depth knowledge of three-dimensional function visualisation techniques
  • To enable delegates to customise plot legends and colorbars for effective data representation
  • To teach the construction of arrays, working with Python lists and custom arrays
  • To enhance data manipulation skills, including reorganising multi-indices and merging datasets

Upon completing these Data Science Courses, delegates will benefit from an enriched skill set in Python for data science, enabling them to tackle complex data analysis tasks with confidence. They will gain proficiency in visualising and interpreting data, ultimately enhancing their value as professionals in the rapidly evolving field of data science.

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What’s included in this Python Data Science Training Course?

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

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Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Probability and Statistics for Data Science​ Training Course Outline

Module 1: Basic Probability Theory

  • Probability Spaces
  • Conditional Probability
  • Independence

Module 2: Random Variables

  • What are Random Variables?
  • Discrete Random Variables
  • Continuous Random Variables
  • Conditioning on an Event
  • Functions of Random Variables
  • Generating Random Variables

Module 3: Multivariate Random Variables

  • Introduction to Multivariate Random Variables
  • Discrete Random Variables
  • Continuous Random Variables
  • Joint Distributions of Discrete and Continuous Variables
  • Independence
  • Functions of Several Random Variables
  • Generating Multivariate Random Variables
  • Rejection Sampling

Module 4: Expectation

  • Expectation Operator
  • Mean and Variance
  • Covariance
  • Conditional Expectation

Module 5: Random Processes

  • Introduction to Random Process
  • Mean and Autocovariance Functions
  • Independent Identically-Distributed Sequences Gaussian Process
  • Poisson Process
  • Random Walk

Module 6: Convergence of Random Processes

  • Types of Convergence
  • Law of Large Numbers
  • Central Limit Theorem
  • Monte Carlo Simulation

Module 7: Markov Chains

  • Markov Property
  • Time-Homogeneous Discrete-Time Markov Chains
  • Recurrence
  • Periodicity
  • Convergence
  • Markov-Chain Monte Carlo

Module 8: Descriptive Statistics

  • What is Descriptive Statistics?
  • Examples of Descriptive Statistics
  • Types of Descriptive Statistics

Module 9: Frequentist Statistics

  • Mean Square Error
  • Consistency
  • Confidence Intervals
  • Nonparametric Model Estimation
  • Parametric Model Estimation
  • Maximum Likelihood

Module 10: Bayesian Statistics

  • Bayesian Parametric Models
  • Conjugate Prior
  • Bayesian Estimators

Module 11: Hypothesis Testing

  • Hypothesis-Testing Framework
  • Parametric Testing
  • Nonparametric Testing: The Permutation Test
  • Multiple Testing

Module 12: Linear Regression

  • Introduction to Linear Regression
  • Linear Models
  • Least-Squares Estimation

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Who should attend this Probability and Statistics for Data Science Training Course?

This Probability and Statistics for Data Science Course is designed to provide foundational and practical knowledge in Probability and Statistics, which are crucial for Data Science, Machine Learning, and Data Analysis. The following are some professionals who will benefit from attending this Course:

  • Data Scientists
  • Machine Learning Engineers
  • Data Analysts
  • Business Analysts
  • Product Managers
  • Quantitative Analysts
  • Statisticians

Prerequisites of the Probability and Statistics for Data Science Training Course 

There are no prerequisites for Probability and Statistics for Data Science Course.

Probability and Statistics for Data Science Training Course Overview

Probability and statistics form the foundational pillars of data science, providing the necessary tools for understanding uncertainty, variability, and making informed decisions based on data. This training course delves into the fundamental concepts of probability and statistics, emphasising their crucial role in the field of data science. Delegates will explore how these concepts contribute to the extraction of meaningful insights and patterns from data.

Understanding probability and statistics is essential for professionals in the data science domain. Data scientists, analysts, and decision-makers rely on these principles to draw accurate conclusions and predictions from data. Mastery of probability allows for the quantification of uncertainty, while statistics enables the analysis of data patterns and trends.

This 2-day training will empower the delegates with the skills to apply probability and statistics in practical data science scenarios. They will learn key concepts such as probability distributions, hypothesis testing, and regression analysis. The course provides a comprehensive understanding of statistical methods, enabling professionals to make informed decisions and predictions based on data

Course Objectives

  • To represent and analyse uncertain phenomena using a framework
  • To quantify the outcome of the experiment as belonging to a specific event
  • To assign probabilities to each occurrence of interest and an experiment
  • To become accustomed to Markov chains and different statistical types
  • To generate samples from the appropriate conditional distribution
  • To evaluate the occurrence of a particular event that influences another event

Upon completion of this course, the delegates will possess a strong foundation in probability and statistics for data science. They will be equipped with the tools and techniques needed to analyse data effectively, make informed decisions, and contribute meaningfully to data-driven projects within their organisations.

 

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What’s included in this Probability and Statistics for Data Science Training Course?

  • World-Class Training Sessions from Experienced Instructors 
  • Probability and Statistics for Data Science Certificate 
  • Digital Delegate Pack

Show moredown

Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Text Mining Training​ Course Outline

Module 1: Introduction to Text Mining

  • What is Text Mining?
  • Text Mining Systems Architecture

Module 2: Core Text Mining Operations

  • What is Core Text Mining Operations?
  • Text Mining Query Languages

Module 3: Text Mining Pre-Processing Techniques

  • Task-Oriented Approaches

Module 4: Categorisation

  • Text Categorisation Applications
  • Document Representation
  • Knowledge Engineering to TC
  • Using Unlabelled Data
  • Evaluating Text Classifiers

Module 5: Introduction to Clustering

  • Partitioning of Networks
  • Clustering Tasks in Text Analysis
  • Clustering Algorithms
  • Clustering of Textual Data

Module 6: Information Extraction (IE)

  • Define Information Extraction
  • IE Systems Architecture
  • Anaphora Resolution
  • IE Inductive Algorithms
  • Structural Information Extraction (IE)

Module 7: Probabilistic Models for IE

  • Hidden Markov Models
  • Stochastic Context-Free Grammar
  • Maximal Entropy Modelling
  • Conditional Random Fields

Module 8: Pre-Processing Applications

  • HMM to Textual Analysis Applications
  • Using MEMM for IE
  • Applications of CRFs to Textual Analysis MEMM for IE
  • Using SCFG Rules
  • Bootstrapping

Module 9: Presentation-Layer Considerations

  • Browsing
  • Accessing Constraints and Simple Specification Filters at the Presentation Layer
  • Accessing the Underlying Query Language

Module 10: Visualisation Approaches

  • Architectural Considerations
  • Text Mining Visualisation Approaches
  • Visualisation Techniques in Link Analysis

Module 11: Introduction to Link Analysis

  • Automatic Layout of Networks
  • Paths and Cycles in Graphs
  • Centrality
  • Partitioning of Networks
  • Networks Pattern Matching

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Who should attend this Text Mining Training Course?

This Text Mining Training Course is suitable for a wide range of professionals and individuals who want to gain expertise extracting information, sentiment, or patterns from unstructured text-based data. The following professionals can benefit from this Training:

  • Data Scientists
  • Software Engineers
  • Data Analysts
  • Digital Marketers
  • Product Managers
  • Business Intelligence Analysts
  • NLP Engineers

Prerequisites of the Text Mining Training Course

There are no formal prerequisites for attending this Text Mining Course.

Text Mining Training Course Overview

Text Mining is a knowledge-intensive process that is a pivotal skill in today's information-driven world. It involves interacting with text-based document collections using powerful analysis tools to uncover valuable insights and patterns within vast data sources, be it reports, articles, or social media data. In this course, delegates embark on a journey to master text mining, a crucial skill set for professionals navigating the sea of textual information.

Understanding Text Mining is vital for professionals in various domains, including Data Analysts, Market Researchers, Content Creators, and Information Scientists. As digital content continues to explode, the ability to extract meaningful information from text sources becomes a competitive advantage.

The 2-day Data Science Course offers a comprehensive understanding of text mining operations and preprocessing techniques. Delegates will delve into the intricacies of text categorisation, explore significant algorithms, leverage unlabelled data effectively, and evaluate text classifiers for improved accuracy. The training enhances their ability to fine-tune text mining processes through constraints and specification filters

Course Objectives

  • To grasp the fundamentals of text mining and preprocessing techniques
  • To utilise key algorithms for efficient text categorisation
  • To evaluate and enhance text classifiers using unlabelled data
  • To gain expertise in clustering and Information Extraction (IE)
  • To explore techniques for constraint handling and specification filtering
  • To master the application of hidden Markov models and maximal entropy Markov models for Information Extraction

Upon completing these Data Science Courses, delegates will possess a comprehensive skill set in text mining, enabling them to efficiently extract valuable insights from textual data, automate processes, and enhance decision-making in their respective professional domains.

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What’s included in this Text Mining Training Course?

  • World-Class Training Sessions from Experienced Instructors 
  • Text Mining Certificate 
  • Digital Delegate Pack

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Keras Training for Data Scientists Course Outline

Module 1: Introduction to Keras

  • What is Keras?
  • Guiding Principles
  • Installation and Setup
  • Configuration

Module 2: Exploring Models

  • Keras Models
  • Sequential Model
  • Model (Functional API)

Module 3: Overview of Keras Layers

  • Core Layers
  • Convolutional Layers
  • Pooling Layers
  • Locally-Connected Layers
  • Recurrent Layers
  • Pre-Processing Layers
  • Merging Layers
  • Activations Layers
  • Normalisation Layers

Module 4: Pre-Processing

  • Dataset Pre-Processing
  • Dataset Pre-Processing Utilities
    • Image Data Pre-Processing
    • Time Series Data Pre-Processing
    • Text Data Pre-Processing

Module 5: Optimisers

  • SGD
  • RMSprop
  • Adam and AdamW
  • Adadelta
  • Adagrad
  • Adamax
  • Adafactor
  • Nadam
  • Ftrl

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Who should attend this Keras Training for Data Scientists Course?

The Keras Training for Data Scientists Course is tailored for data scientists and professionals who want to gain proficiency in deep learning using the Keras framework. This Data Science Training is particularly beneficial for the following individuals:

  • Data Scientists
  • Machine Learning Engineers
  • Software Developers
  • Deep Learning Engineers
  • Medical Researchers
  • Bioinformaticians
  • Data Journalists

Prerequisites of the Keras Training for Data Scientists Course

There are no formal prerequisites for attending this Keras Training for Data Scientists Course. However, having some prior knowledge of machine learning concepts and Python programming can be beneficial.

Keras Training for Data Scientists Course Overview

Keras is an open-source neural network library that has become a crucial tool for professionals in the data-driven landscape. This course, "Keras Training for Data Scientists," delves into the intricacies of this robust framework. With a Python foundation and seamless integration with TensorFlow, CNTK, and Theano, Keras facilitates rapid experimentation and is essential for data scientists navigating the complexities of modern data science.

Proficiency in Keras is vital for diverse professionals, including Data Scientists, Machine Learning Engineers, Software Developers, Deep Learning Engineers, Medical Researchers, Bioinformaticians, and Data Journalists. Mastering Keras empowers professionals to architect and experiment with these models efficiently as the demand for neural networks and deep learning solutions grows. Its relevance is undeniable in the dynamic landscape of data science.

This 1-day Keras Training for Data Scientists Course is designed to accommodate individuals from diverse backgrounds and industries. Delegates in this will acquire comprehensive knowledge of different Keras layers, ranging from core to recurrent layers. Additionally, they will learn essential preprocessing techniques for sequences, text, and images—critical skills for effectively preparing data for neural network applications.

Course Objectives

  • To grasp the fundamentals of Keras and its integration with deep learning frameworks.
  • To explore various Keras layers for building neural networks.
  • To learn the essentials of preprocessing data for sequence, text, and image applications.
  • To gain proficiency in using regularisers and constraints to enhance model performance.
  • To master the practical aspects of compiling models with loss and optimiser functions.
  • To acquire the skills to architect neural networks efficiently for complex data science problems.

Upon completing this Data Science Course, delegates will possess a robust understanding of Keras, enabling them to efficiently experiment with and architect diverse neural network models. The acquired skills in preprocessing for sequences, text, and images will enhance their ability to tackle complex data science problems.

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What’s included in this Keras Training for Data Scientists Course?

  • World-Class Training Sessions from Experienced Instructors 
  • Keras Training for Data Scientists Certificate 
  • Digital Delegate Pack

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Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Pandas for Data Analysis Training Course Outline

Module 1: Introduction and Installation

  • Define Pandas
  • Installing Pandas
  • Running Test Suite
  • Dependencies

Module 2: Getting Started with Pandas

  • Package Overview
  • Exploring Panda
  • Essential Basic Functionality
  • Comparison with Other Tools

Module 3: User Guide

  • IO Tools
  • Indexing
  • Merge, Join, and Concatenate
  • Reshaping and Pivot Tables
  • Working with Text Data, Missing Data, and Categorical Data
  • Nullable Integer Data Type
  • Computational Tools
  • Group By: Split-Apply-Combine
  • Time Series and Time Deltas
  • Stylings
  • Options and Settings
  • Enhancing Performance
  • Sparse Data Structures

Module 4: Pandas Ecosystem

  • Statistics and Machine Learning
  • Visualisation
  • IDE
  • Extension Data Types

Module 5: Development Phase

  • Extending Pandas
  • Storing Pandas DataFrame Objects in Apache Parquet Format

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Who should attend this Pandas for Data Analysis Training Course?

The Pandas for Data Analysis Course aims at equipping Delegates with the knowledge and skills to manipulate, analyse, and visualise data using the Pandas library in Python. The following professionals can benefit greatly with this course:

  • Data Analysts
  • Data Scientists
  • Business Analysts
  • Researchers
  • Statisticians
  • Data Engineers
  • Machine Learning Engineers

Prerequisites of the Pandas for Data Analysis Training Course

There are no formal prerequisites to attend Pandas for Data Analysis Course. However, a basic knowledge of programming can be beneficial in this Data Analysis Training.

 

Pandas for Data Analysis Training Course Overview

In the realm of data science, Pandas stands as a beacon of efficiency and versatility. This open-source Python library offers high-performance tools for data analysis and data structure manipulation. With its extensive capabilities, Pandas has become indispensable in various fields, including statistics, economics, and analytics.

Data Scientists, Analysts, Economists, and anyone dealing with data in Python can greatly benefit from mastering this versatile library. Pandas simplifies data manipulation tasks, allowing professionals to clean, transform, and analyse data efficiently. In an era where data quality and speed are essential, Pandas empowers professionals to make informed decisions, extract valuable insights, and drive business success.

Over the course of this intensive 2-day training by the Knowledge Academy, delegates will embark on a comprehensive journey through Pandas and its data structures. They will gain a deep understanding of how to work with various data types, including text, missing, and categorical data, enabling them to handle diverse datasets effectively. Delegates will also explore essential Pandas operations such as merging, joining, concatenating, reshaping, etc.

Course Objectives

  • To master Pandas data structures and operations.
  • To efficiently handle text, missing, and categorical data.
  • To explore essential Pandas functions for merging, reshaping, and pivot tables.
  • To acquire data visualisation skills and utilise integrated development environments.
  • To understand data validation practices and work with extension data types.
  • To gain proficiency in storing Pandas Data Frame objects in Apache Parquet format.

Upon completing this Data Science Course, delegates will benefit from enhanced proficiency in data analysis using Pandas. They will be equipped with the skills to efficiently manipulate diverse datasets, perform essential Pandas operations, and utilise data visualisation techniques.

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What’s included in this Pandas for Data Analysis Training Course?

  • World-Class Training Sessions from Experienced Instructors 
  • Pandas for Data Analysis Certificate 
  • Digital Delegate Pack

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Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Predictive Analytics Training​ Course Outline

Module 1: Introduction to Predictive Analytics

  • What is Predictive Analytics?
  • How Predictive Analytics Works?
  • Business Intelligence
  • Business Intelligence Lifecycle
  • Predictive Analytics Vs Business Intelligence
  • Predictive Analytics Challenges

Module 2: Setting Up the Problem

  • Predictive Analytics Processing Steps
  • Target Variable
  • Success Measures for Predictive Models
  • Data for Predictive Modelling

Module 3: Understanding the Data

  • Single and Multiple Variables          
  • Data Visualisation
  • Histograms

Module 4: Data Preparation

  • Data Cleaning
  • Feature Creation

Module 5: Itemsets and Association Rules

  • Association Rules
  • Parameter Settings
  • Data Organisation Techniques
  • Deploying Association Rules
  • Making Classification Rules from Association Rules

Module 6: Descriptive Modelling

  • Introduction to Descriptive Modelling
  • Principal Component Analysis
  • Clustering Algorithms

Module 7: Interpreting Descriptive Models

  • Introduction to Descriptive Analytics
  • How does Descriptive Analytics Work?
  • Advantages of Descriptive Analytics

Module 8: Predictive Modelling

  • Introduction to Predictive Modelling
  • Decision Tree
  • Types of Decision Tree
  • Logistic Regression
  • Neural Networks
  • K-Nearest Neighbour
  • Naïve Bayes
  • Linear Regression

Module 9: Predictive Models Assessment

  • Introduction to Predictive Models Assessment
  • Metrics to Evaluate Predictive Models

Module 10: Model Ensembles

  • Ensemble Modelling
  • Types of Model Ensembles
  • Bias Variance Trade-Off
  • Bagging and Boosting
  • Interpreting Model Ensembles

Module 11: Text Mining

  • Introduction to Text Mining
  • Structured Vs Unstructured Data
  • Text Mining Applications
  • Steps of Data Preparation
  • Regular Expressions

Module 12: Predictive Model Deployment

  • Introduction to Predictive Model Deployment
  • Building Predictive Models

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Who should attend this Predictive Analytics Course?

This Predictive Analytics Training Course aims to provide professionals the core concepts, techniques, and tools used to predict future events based on historical data and delves into the various stages of the analytics life cycle. This training can help various professionals, including:

  • Data Scientists
  • Business Analysts
  • Marketing Analysts
  • Financial Analysts
  • Business Intelligence Professionals
  • Risk Managers
  • CRM Professionals

Prerequisites of the Predictive Analytics Training Course

There are no formal prerequisites required for attending this Predictive Analytics Training Course.

Predictive Analytics Training Course Overview

Predictive Analytics is a powerful tool in the data science domain that plays a crucial role in foreseeing unknown future events that organisations must navigate. Utilising techniques like data mining, modelling, statistics, artificial intelligence, and machine learning, predictive analytics empowers organisations to identify forthcoming risks and opportunities by deciphering patterns within transactional and historical data.

Proficiency in Predictive Analytics is indispensable for a range of professionals, including Data Scientists, Business Analysts, Marketing Experts, Financial Analysts, and decision-makers in diverse industries. As organisations seek a competitive edge, Predictive Analytics offers a strategic advantage, allowing professionals to anticipate market trends, customer behaviors, and operational efficiencies.

The 2-day course provides delegates with knowledge on essential aspects like data cleaning, feature creation, item sets, and association rules. This training equips delegates with the knowledge to uncover valuable patterns and associations within data. Regardless of their field, individuals keen on augmenting their data science skill set will benefit from this training, gaining familiarity with predictive modeling techniques
Course Objectives

  • To master the predictive analytics process, including data cleaning and feature creation.
  • To gain proficiency in uncovering valuable patterns and associations using itemsets and association rules.
  • To explore various predictive modelling techniques, such as logistic regression and Naïve Bayes.
  • To develop the skills to apply predictive models effectively in real-world scenarios.
  • To enhance decision-making by leveraging data-driven predictions and insights.
  • To empower organisations to become forward-looking and proactive using predictive analytics.

By the end of this Data Science Course, the delegates will possess the skills to develop and apply predictive models effectively, enabling them to make data-driven predictions, optimise decision-making processes, and unlock new avenues for organisational growth.

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What’s included in this Predictive Analytics Training Course?

  • World-Class Training Sessions from Experienced Instructors
  • Predictive Analytics Certificate
  • Digital Delegate Pack

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Introduction to Knime Analytics Platform Training Course Outline

Module 1: Installing KNIME Analytics Platform

  • KNIME Analytics Platform Installation
  • Extensions and Integrations Installation
  • Update KNIME Analytics Platform and Extensions
  • Update Sites

Module 2: Introduction to KNIME Analytics Platform

  • Building Workflows
  • Nodes and Workflows
  • Extensions and Integrations

Module 3: KNIME Workbench

  • Overview of KNIME Workbench
  • Customising KNIME Workbench
  • Configure KNIME Analytics Platform
  • KNIME Tables

Module 4: KNIME Flow Control

  • Introduction
  • Flow Variables
  • Loops
  • IF and CASE Switches
  • Error Handling

Module 5: KNIME Extensions and Integrations

  • Introduction to KNIME Extensions and Integrations
  • Community Extensions

Module 6: CSS Styling for JavaScript Views and QuickForm Nodes

  • Setup
  • Description of CSS Classes
  • Classes by Node

Module 7: Creating New KNIME Extension

  • Setting KNIME SDK
  • Creating New KNIME Extension Project
  • Project Structure
  • Deploying Extension

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Who should attend this Introduction to Knime Analytics Platform Training Course?

The Introduction to Knime Analytics Platform Course is designed to provide an in-depth understanding of the Knime platform, a leading open-source data analytics, reporting, and integration tool. This course can be beneficial for various professionals, including:

  • Data Scientists
  • Data Analysts
  • Data Engineers
  • Data Visualisation Experts
  • Machine Learning Engineers
  • Database Administrators
  • Marketing Analysts

Prerequisites of the Introduction to Knime Analytics Platform Training Course

There are no formal prerequisites required for attending this Introduction to Knime Analytics Platform Training Course.

Introduction to Knime Analytics Platform Course Overview

In the realm of data science, Knime Analytics Platform shines as an open-source software that empowers individuals to create data science applications and services seamlessly. It's the key to unravelling the power of data, enabling the understanding of data intricacies, designing data science workflows, and building reusable components accessible to everyone.

Proficiency in the Knime Analytics Platform is a significant asset for a broad spectrum of professionals. Data scientists, data analysts, business intelligence professionals, and anyone dealing with data in various industries can greatly benefit from mastering Knime. With the exponential growth of data, the ability to create visual workflows and perform data analysis without coding is a game-changer.

In this intensive 1-day training course, delegates will embark on a comprehensive journey through the Knime Analytics Platform. They will gain practical knowledge on how to install and update the platform and its extensions, ensuring they are up to date with the latest tools. Delegates will become well-versed in navigating the Knime workbench and handling Knime tables, essential for data manipulation and analysis.

Course Objectives

  • To empower individuals with the skills to create data science applications using the Knime Analytics Platform
  • To enable understanding of data intricacies and the design of code-free, drag-and-drop visual workflows
  • To cater to a broad spectrum of professionals, including data scientists, analysts, and business intelligence professionals
  • To provide practical knowledge on installing, updating, and navigating the Knime Analytics Platform
  • To equip delegates with the ability to handle Knime tables for data manipulation and analysis

Upon completing this Data Science Course, delegates will possess the proficiency to create data science applications effortlessly using the Knime Analytics Platform. They will be equipped with the skills to navigate, manipulate, and analyse data efficiently, fostering the ability to automate data processes.

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What’s included in this Introduction to Knime Analytics Platform Training Course?

  • World-Class Training Sessions from Experienced Instructors
  • Introduction to Knime Analytics Platform Certificate
  • Digital Delegate Pack

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Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Data Mining Training​ Course Outline

Module 1: Getting Started with Data Mining

  • Introduction to Data Mining
  • Types of Data
  • Data Objects and Attribute Types
  • Data Visualisation
  • Measuring Data Similarity and Dissimilarity

Module 2: Data Preprocessing

  • Introduction to Data Preprocessing
  • Data Integration
  • Data Cleaning
  • Data Reduction
  • Data Transformation
  • Data Discretisation

Module 3: Data Warehousing and Online Analytical Processing

  • Basic Concepts of Data Warehousing
  • Data Cube and OLAP
  • Design of Data Warehouse
  • Implementation of Data Warehouse
  • Uses of Data Warehouse

Module 4: Data Cube

  • Preliminary Concepts
  • Data Cube Computation Methods
  • Multidimensional Data Analysis in Cube Space

Module 5: Mining Frequent Patterns, Associations, and Correlations

  • Frequent Itemset Mining Methods
  • Pattern Evaluation Methods

Module 6: Advanced Pattern Mining

  • Pattern Mining in Multilevel and Multidimensional Space
  • Constraint-Based Frequent Pattern Mining
  • Mining High-Dimensional Data and Colossal Patterns
  • Mining Compressed or Approximate Patterns
  • Pattern Exploration and Application

Module 7: Classification

  • Introduction to Classification
  • Decision Tree Induction
  • Bayes Classification Methods
  • Rule-Based Classification

Module 8: Advanced Methods of Classification

  • Backpropagation
  • Classification Using Frequent Patterns
  • Lazy Learners
  • Genetic Algorithms, Rough Set Approach, and Fuzzy Set Approaches

Module 9: Cluster Analysis

  • Introduction to Cluster Analysis
  • Partitioning and Hierarchical Methods
  • Density-Based and Grid-Based Methods

Module 10: Advanced Cluster Analysis

  • Probabilistic Model-Based Clustering
  • Clustering High-Dimensional and Graph Data
  • Clustering with Constraints

Module 11: Outlier Detection

  • Outlier Analysis
  • Outlier Detection Methods
  • Statistical and Proximity-Based Approaches
  • Clustering-Based and Classification-Based

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Who should attend this Data Mining Training Course?

The Data Mining Course is tailored to impart knowledge on the process of extracting patterns, correlations, and information from large amounts of data stored in databases, data warehouses, or other information repositories. This course can be beneficial for various professionals aiming to derive insights from data, including:

  • Data Scientists
  • Business Analysts
  • Database Administrators
  • Marketing Analysts
  • Researchers
  • Statisticians
  • Machine Learning Engineers

Prerequisites of the Data Mining Training Course

There are no formal prerequisites required for attending this Data Mining Course. Although, a prior understanding of IT concepts can be helpful.

Data Mining Training Course Overview

Data mining is the method of detecting patterns in large data sets by making use of statistics, machine learning and database systems. It includes analysing large amounts of data and converting it into useful information. The insights gained from data mining can be used for fraud detection, marketing, scientific discovery, etc.

This Data Science Course will provide delegates with extensive knowledge on data mining. This course will cover the main concepts of data mining, including data objects, data visualisation, measuring data similarity, and data preprocessing. Delegates will also learn about data transformation and data discretisation. Data warehousing and online analytical processing will also be crucial concepts of this course, including basic data warehousing concepts, data cube, and OLAP.

In addition, this 2-day training course will cover mining frequent patterns, associations, and correlations including pattern evaluation methods. Delegates will acquire knowledge on advanced pattern mining that comprises constraint-based frequent pattern mining, mining high-dimensional data and colossal patterns, and pattern exploration and application. By the end of this course, delegates will have gained comprehensive knowledge on classification methods, cluster analysis, and outlier detection.

Course Objectives:

  • To comprehend data mining concepts and its practical applications
  • To acquire data preprocessing skills for effective analysis
  • To grasp data transformation and discretisation techniques
  • To understand data warehousing and online analytical processing (OLAP)
  • To become proficient in frequent pattern mining and associations
  • To develop expertise in advanced pattern mining, classification, clustering, and outlier detection

Upon completing this Data Mining Training Course, delegates will gain advanced skills in extracting valuable insights from large datasets, enhancing their ability to make informed business decisions. Additionally, the course equips them with practical knowledge of cutting-edge data mining techniques.

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What’s included in this Data Mining Training Course?

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

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

GIS Development Training Course Outline

Module 1: Introduction to Geographic Information Systems (GIS)

  • What is GIS?
  • GIS Terminologies
  • Overview of ArcGIS Pro
  • Data Representations in ArcGIS Pro
  • Desktop GIS Software Packages
  • GIS Analyst Skills
  • Installing ArcGIS Desktop

Module 2: Basics of ArcGIS

  • Explore Data Using ArcGIS Pro
  • View and Change Layer Properties
  • Feature Classes and Attribute Tables
  • Select by Attribute and Calculate Geometry
  • Select by Location
  • Define Projections
  • Analyse Data with Geoprocessing Tools
  • Geoprocessing Environment Setting
  • Assess Spatial Relationships with Spatial Join Tool

Module 3: Making Maps with Common Datasets

  • Core Map Elements
  • Symbology: Changing How Data Looks
  • Setting Symbology in ArcGIS
  • Labelling Map Features
  • Making Map Books

Module 4: Retrieving and Sharing Data

  • Using Metadata to Document Data Products
  • Sharing Data and Maps
  • Joins and Relates

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Who should attend this GIS Development Training Course?

The GIS Development Course offers professionals the knowledge of the concepts, tools, and techniques necessary for developing Geographic Information Systems (GISs). This course can be beneficial for various professionals, including:

  • GIS Developers
  • GIS Analysts
  • Geospatial Engineers
  • Remote Sensing Specialists
  • Infrastructure Planners
  • Natural Resource Managers
  • Landscape Architects

Prerequisites of the GIS Development Training Course

There are no formal prerequisites required for attending this GIS Development Training Course.

GIS Development Training Course Overview

In our increasingly interconnected world, Geographic Information Systems (GIS) have become the bedrock of data-driven decision-making. GIS is more than just a mapping tool; it's a powerful framework that enables the collection, management, analysis, and visualisation of spatial data. By integrating various data types and harnessing the power of spatial location, GIS transforms layers of information into insightful maps and 3D scenes.

Proficiency in GIS is invaluable for professionals across diverse domains. Urban planners, environmental scientists, data analysts, disaster management experts, and anyone dealing with location-based data can benefit significantly from mastering GIS. In a world where location data plays a pivotal role, GIS empowers professionals to make informed decisions, optimise resource allocation, and visualise complex information in a spatial context.

In this intensive 1-day GIS Development Training course, delegates will embark on a journey through the fundamentals of Geographic Information Systems. They will explore the realm of spatial analysis and cartography, gaining insights into the core concepts of GIS. Delegates will become familiar with ArcGIS, the leading software tool in the GIS domain, and understand how GIS has evolved from traditional paper maps to globally integrated electronic software packages.

Course Objectives

  • To provide a comprehensive understanding of Geographic Information Systems (GIS) fundamentals
  • To explore the realm of spatial analysis and cartography within the GIS domain
  • To familiarise delegates with ArcGIS, the leading software tool for GIS
  • To demonstrate the evolution of GIS from traditional paper maps to electronic software packages
  • To equip delegates with the knowledge to analyse data using geoprocessing tools
  • To guide delegates in creating and using map packages, uploading them to ArcGIS online, and creating layer files and packages

Upon completion of this course, delegates will possess a solid understanding of GIS fundamentals and proficiency in using ArcGIS, the industry-leading software. They will be able to analyse spatial data, create insightful maps, and leverage GIS capabilities for informed decision-making.

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What’s included in this GIS Development Training Course?

  • World-Class Training Sessions from Experienced Instructors
  • GIS Development Certificate
  • Digital Delegate Pack

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Decision Tree Modeling Using R Training​ Course Outline

Module 1: Introduction to Decision Tree

  • Decision Tree Modelling Objective
  • Anatomy of a Decision Tree
  • Important Terminology Related to Decision Trees

Module 2: Overview of R Programming

  • R Programming Language
  • Data Types
  • Control Structures in R

Module 3: Data Treatment Before Modelling

  • Data Sanity Check-Contents
  • View
  • Frequency Distribution
  • Uni-Variate
  • Categorical Variable Treatment

Module 4: Classification of Tree Development and Algorithm Details

  • Installing R Package and R studio
  • Developing First Decision Tree in R Studio
  • Find Strength of the Model

Module 5: Decision Tree Analysis in Project Management

  • Use Decision Tree in Project Management
  • Tools for Decision Tree Analysis
  • Decision Tree Analysis Strategy

Module 6: Regression Tree and Auto Pruning

  • Introduction to Pruning
  • Understand K Fold Validation for Model 
  • Develop Regression Tree 
  • How is it Different from Linear Regression? 
  • Advantages and Disadvantages over Linear Regression

Module 7: CHAID Algorithm

  • What is CHAID/CART Algorithm?
  • Chi-Square Statistics 
  • Implement Chi-Square for Decision Tree Development 
  • CHAID Vs CART

Module 8: Other Algorithms

  • ID3
  • Random Forest Method
  • Using R for Random Forest Method

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Who should attend this Decision Tree Modelling Using R Training Course?

The Decision Tree Modelling Using R Training Course offers a detailed exploration of decision tree models, one of the most widely used algorithms in Machine Learning and Data Science. This course can be beneficial for various professionals, including:

  • Data Scientists
  • Machine Learning Engineers
  • Data Analysts
  • Research Scientists
  • Quantitative Researchers
  • Risk Assessment Managers
  • Predictive Modelers

Prerequisites of the Decision Tree Modelling Using R Training Course

There are no formal prerequisites for attending this Decision Tree Modelling Using R Training Course. However, having some knowledge of the R programming language will be helpful.

Decision Tree Modelling Using R Training  Course Overview

Decision Tree Modelling Using R is a formidable analytical technique with wide-ranging applications across diverse industries, including finance, automotive, and telecommunications. It serves as a powerful tool for making data-driven decisions, enabling businesses to navigate complex scenarios and optimise outcomes.

Proficiency in Decision Tree Modelling Using R is essential for professionals seeking to enhance their data science skills and make informed decisions rooted in data analysis. Data Scientists, Business Analysts, Financial Experts, and professionals in various industries can greatly benefit from mastering this technique. In an era where data-driven decision-making is paramount, Decision Tree Modelling equips professionals with the ability to dissect data, identify patterns, and derive actionable insights.

This intensive 1-day Decision Tree Modelling Using R Certification course immerses delegates in a comprehensive exploration of Decision Tree Modelling concepts. They will embark on a journey starting from the fundamentals of Decision Trees, progressing to advanced topics such as data design for modelling, algorithm details, industry best practices, validation techniques, and practical applications using the R programming language.

Course Objectives

  • To understand the fundamentals of Decision Tree Modelling
  • To learn data treatment and frequency distribution techniques
  • To explore Decision Tree algorithm development and pruning
  • To gain expertise in advanced topics like Random Forest and CHAID Algorithm
  • To acquire practical skills in using R for Decision Tree Modelling
  • To become proficient in applying Decision Tree Modelling to real-world scenarios

By the end of this course, delegates will emerge with expertise in Decision Tree Modelling using R, empowering them to leverage this powerful tool for data analysis and informed decision-making.

 

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What’s included in this Decision Tree Modelling Using R Training Course?

  • World-Class Training Sessions from Experienced Instructors
  • Decision Tree Modelling Using R Certificate
  • Digital Delegate Pack

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

PySpark Training​ Course Outline

Module 1: Introduction to PySpark

  • What is PySpark?
  • Environment
  • Spark Dataframes
  • Reading Data
  • Writing Data
  • MLlib

Module 2: Installation

  • Using PyPI
  • Using PySpark Native Features
  • Using Virtualenv
  • Using PEX
  • Dependencies

Module 3: DataFrame

  • DataFrame Creation
  • Viewing Data
  • Applying a Function
  • Grouping Data
  • Selecting and Accessing Data
  • Working with SQL
  • Get () Method

Module 4: Setting Up a Spark Virtual Environment

  • Understanding the Architecture of Data-Intensive Applications
  • Installing Anaconda
  • Setting a Spark Powered Environment
  • Building App with PySpark

Module 5: Building Batch and Streaming Apps with Spark

  • Architecting Data-Intensive Apps
  • Build a Reliable and Scalable Streaming App
  • Process Live Data with TCP Sockets
  • Analysing the CSV Data
  • Exploring the GitHub World
  • Previewing App

Module 6: Learning from Data Using Spark

  • Classifying Spark MLlib Algorithms
  • Spark MLlib Data Types
  • Clustering the Twitter Dataset
  • Build Machine Learning Pipelines

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Who should attend this PySpark Training Course?

This PySpark Course covers the fundamentals of Spark, its architecture, and how to use the PySpark API for Data Processing, Analytics, and Machine Learning tasks. This course can be beneficial for various professionals, including:

  • Data Engineers
  • Big Data Analysts
  • Data Scientists
  • Machine Learning Engineers
  • Software Developers
  • Python Developers
  • Solution Architects
  • System Administrators
  • Database Administrators

Prerequisites of the PySpark Training Course

There are no formal prerequisites required for attending this PySpark Course.

PySpark Training Course Overview

PySpark Training is a crucial component in the arsenal of data scientists, business analysts, and professionals across various industries. PySpark, a Python API for Apache Spark, is a powerful framework for big data processing and analytics. Its relevance lies in its ability to handle large-scale data processing tasks efficiently, making it an essential skill for those navigating the dynamic landscape of data science.

Professionals aiming to master PySpark include Data Scientists, Data Engineers, and analysts dealing with big data. In an era where large datasets are the norm, the capability to leverage PySpark for data processing, machine learning, and analytics is paramount. This course is tailored to empower individuals with the skills needed to harness the potential of PySpark, making it an indispensable asset for professionals seeking to stay ahead in this domain.

This 1-day training by the Knowledge Academy provides delegates with a deep dive into PySpark, covering fundamentals, advanced topics, and practical applications. From understanding the basics of PySpark to exploring its capabilities in big data analytics, delegates will gain hands-on experience. The training aims to equip professionals with the knowledge and skills needed to efficiently process large-scale data using PySpark.

Course Objectives:

  • To provide a comprehensive understanding of PySpark fundamentals
  • To cover advanced topics such as big data analytics using PySpark
  • To offer hands-on experience in applying PySpark for data processing and analytics
  • To equip professionals with the skills to efficiently handle large-scale data processing tasks
  • To empower delegates to leverage PySpark for machine learning applications

Upon completion of this course, the delegates will possess the skills to effectively utilise PySpark for big data processing and analytics. They will have hands-on experience in applying PySpark for machine learning applications, enhancing their proficiency in handling large-scale data tasks.

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What’s included in this PySpark Training Course?

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

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Online Instructor-led (4 days)

Classroom (4 days)

Online Self-paced (32 hours)

Advanced Data Science Certification​ Course Outline

Module 1: Python for Data Analysis - NumPy

  • Introduction to NumPy
  • NumPy Arrays
  • Aggregations
  • Computation on Arrays: Broadcasting
  • Comparison, Boolean Logic and Masks
  • Fancy Indexing
  • Sorting Arrays
  • NumPy’s Structured Arrays

Module 2: Python for Data Analysis – Pandas

  • Installing Pandas
  • Pandas Objects
  • Data Indexing and Selection
  • Operating on Data in Pandas
  • Handling Missing Data
  • Hierarchical Indexing
  • Concat and Append
  • Merge and Join
  • Aggregations and Grouping
  • Pivot Tables
  • Vectorised String Operations
  • Working with Time Series
  • Eval() and Query()

Module 3: Python for Data Visualisation – Matplotlib

  • Overview
  • Object-Oriented Interface
  • Two interfaces
  • Simple Line Plots and Scatter Plots
  • Visualising Errors
  • Contour Plots
  • Histograms, Binnings and Density
  • Customising Plot Legends
  • Customising Colour Bars
  • Multiple Subplots
  • Text Annotation
  • Three-Dimensional Plotting

Module 4: Python for Data Visualisation – Seaborn

  • Installing Seaborn and Load Dataset
  • Plot the Distribution
  • Regression Analysis
  • Basic Aesthetic Themes and Styles
  • Distinguish between Scatter Plots, Hexbin Plots and KDE Plots
  • Use Boxplots and Violin Plots
  • Compare the Use Cases of Swarn Plots, Bar Plots Strip Plots, and Categorical Plots
  • Recall Some of the Use Cases and Features of Seaborn

Module 5: Machine Learning

  • Introduction
  • Importance
  • Types
  • How Machine Learning Works?
  • Machine Learning Mathematics

Module 6: Natural Language Processing

  • Introduction to NLP
  • NLP and Writing Systems
  • Advantages
  • NLP Applications

Module 7: Deep Learning

  • Introduction
  • Importance
  • Working

Module 8: Big Data

  • Big Data Analytics
  • State of Practice in Analytics
  • Main Roles for New Big Data Ecosystem
  • Phases of Data Analytics Lifecycle

Module 9: Working with Data in R

  • Data Manipulation in R
  • Data Clean Up
  • Reading and Exporting Data
  • Importing Data
  • Charts and Graphs

Module 10: Regression in R

  • Regression Analysis
  • Linear Regression
  • Logistic Regression
  • Multiple Regression
  • Normal Distribution
  • Binomial Distribution

Module 11: Modelling Data

  • What are the Relationships?
  • Viewing Relationships
  • Creating Relationships
  • Cardinality
  • Cross Filter Direaction
  • What is DAX?
  • Syntax
  • Functions
  • Row Context
  • Calculated Columns
  • Calculated Tables
  • Measures

Module 12: Shaping and Combining Data using Power BI

  • Query Editor
  • Shaping Data and Applied Steps
  • Advanced Editor
  • Formatting Data
  • Transforming Data
  • Combining Data

Module 13: Interactive Data Visualisations

  • Page Layout and Formatting
  • Multiple Visualisation
  • Creating Charts
  • Using Geographic Data
  • Histograms
  • Power BI Admin Portal
  • Service Settings
  • Desktop Settings
  • Dashboard and Report Settings

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Who should attend this Advanced Data Science Certification Course? 

This Advanced Data Science Certification Course is tailored for individuals seeking to enhance their expertise in the field of data science. This training is particularly beneficial for professionals such as:

  • Experienced Data Analysts
  • Data Scientists
  • Machine Learning Engineers
  • Statisticians
  • Business Analysts
  • AI Developers
  • Entrepreneurs
  • Researchers

Prerequisites of the Advanced Data Science Certification Course

There are no formal prerequisites for the Advanced Data Science Certification Course. However, having prior knowledge of multiple programming languages will be beneficial for the delegates.

Advanced Data Science Certification Course Overview

In today's data-driven world, the demand for professionals with expertise in data science is soaring, and this Advanced Data Science Certification stands as a beacon for individuals seeking to navigate the intricate landscape of data science. Covering critical topics such as NumPy arrays, regression analysis, machine learning mathematics, and more, this  training provides a robust foundation for those eager to harness the power of data.

Business Analysts, Data Engineers, Software Developers, and even executives can benefit significantly from mastering data science. In an era where every bit of data holds invaluable insights, those well-versed in data science possess a distinct advantage. They can unravel complex patterns, make informed decisions, and drive innovation.

This 4-day Advanced Data Science Certification Course is designed to empower delegates with practical knowledge and hands-on skills. It delves into essential concepts such as working with time series, three-dimensional plotting, data analytics lifecycle phases, and data manipulation using Power BI. By the end of this training, delegates will emerge as adept data scientists capable of working with data at expert-level

Course Objectives

  • To provide a comprehensive understanding of basic and advanced data science concepts
  • To equip delegates with practical skills in tools such as Pandas and Power BI
  • To cover critical topics like NumPy arrays, regression analysis, and machine learning mathematics
  • To empower business analysts, data engineers, software developers, and executives with data science proficiency
  • To enable delegates to unravel complex patterns and make informed decisions through data-driven insights
  • To ensure delegates emerge as adept data scientists capable of tackling real-world data challenges

Upon completion of this  training, delegates will not only possess a solid foundation in data science but also practical skills in tools like Pandas and Power BI. This empowers them to analyse data effectively, make informed decisions, and contribute meaningfully to their organisations' success.

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

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

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Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Data Science with R Training Course Outline

Module 1: Introduction to R

  • What is R?
  • R Installation and Dashboard Review
  • Variables
  • Data Types
  • Operators
  • Conditional Statements
  • Looping Statements
  • Functions

Module 2: Data Structures in R

  • What are Data Structures?
  • Vectors
  • Lists
  • Matrix
  • Arrays
  • Dataframes

Module 3: Working with Data in R

  • Types of Files in R
  • Working with CSV
  • Working with Excel Files
  • Working with JSON Files
  • Working with XML Files

Module 4: Data Manipulation in R

  • What is Data Manipulation?
  • Installation of Dplyr Package
  • Data Manipulation Operations in R

Module 5: Data Visualisations in R

  • What is Data Visualisation?
  • Working with Graphs and Plots in R

Module 6: Statistics in R

  • Introduction to Statistics in R
  • Introduction to Descriptive Statistics
  • Distributions in R

Module 7: Machine Learning

  • Introduction to Machine Learning in R
  • Types of Machine Learning in R
  • Introduction to Supervised Learning in R
  • Introduction to Unsupervised Learning in R

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Who should attend this Data Science with R Training Course?

The Data Science with R Training Course is designed for professionals, analysts, and individuals who are interested in acquiring skills and knowledge in data analysis, visualisation, and modeling using the R programming language. This course is beneficial for various professionals including:

  • Data Analysts
  • Data Scientists
  • Statisticians
  • Business Analysts
  • Quantitative Finance Professionals
  • Epidemiologists and Public Health Experts
  • Data Engineers
  • Geographic Information System (GIS) Analysts

Prerequisites for the Data Science with R Training Course

There are no formal prerequisites for attending this Data Science with R Training Course.

Data Science with R Training Course Overview

Data Science and the R programming language collaborate seamlessly to dissect and manipulate data. R serves as the preferred programming language for Data Scientists, enabling them to preprocess data, construct predictive models, perform statistical analyses, and craft insightful data visualisations. This training equips individuals with the skills to leverage the R language effectively, transforming raw data into valuable insights and actionable recommendations.

Mastering Data Science with R enables effective data preprocessing, statistical analysis, and predictive modelling. This proficiency is especially valuable for Data Scientists, Analysts, and Researchers working with complex datasets. The versatility of R empowers professionals to extract meaningful insights and contribute to data-driven decision-making in various industries, making it an essential skill set for career advancement.

The Knowledge Academy's 2-day Data Science with R Training Course delivers comprehensive knowledge of R programming and its mastery. Delegates will delve into working with vectors, lists, metrics, arrays, and data frames, in addition to acquiring expertise in data manipulation, the art of refining and adapting data for enhanced analytical capabilities.

Course Objectives:

  • To provide a comprehensive understanding of the R programming language in the context of Data Science
  • To equip delegates with the skills to preprocess data, construct predictive models, and perform statistical analyses using R
  • To empower individuals to effortlessly conduct complex computations and statistical analyses using R tools
  • To deliver expertise in working with essential tools like data frames, matrices, vectors, and lists in the R language
  • To enhance proficiency in data manipulation for refining and adapting data for analytical capabilities
  • To ensure mastery of R programming through hands-on training and practical applications in Data Science

Upon completion of this Data Science with R Training, delegates will possess comprehensive knowledge and mastery of the R programming language. They will be equipped with the skills to preprocess data, construct predictive models, and perform statistical analyses, enhancing their Data Science capabilities.

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What’s included in this Data Science with R Training Course?

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

Show moredown

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Data Science and Blockchain Training Course Outline

Module 1: Introduction to Data Science

  • What is Data Science?
  • How Does Data Science Work?
  • Data Science Life Cycle
  • Roles and Responsibilities of a Data Scientist
  • Importance of Data Science
  • Data Science Applications
  • Business Intelligence Vs Data Science

Module 2: Blockchain Overview

  • Blockchain Technology
  • Why is Blockchain Important?
  • How Does Blockchain Work?
  • Decentralisation in Blockchain
  • Blockchain Uses
  • Blockchain Applications

Module 3: Implications of Blockchain in Data Science

  • Relationship Between Blockchain and Data Science
  • How Blockchain can Help Big Data?
  • How Blockchain will Enhance Data Science?

Module 4: Blockchain in Big Data Transformation

  • Introduction
  • What is a Blockchain, and How Does it Work?
  • Bringing Blockchain and Big Data Together
  • Things That Blockchain Transforms Big Data

Module 5: Blockchain Storage

  • What is Blockchain Storage?
  • What Will Blockchain Mean for Data Storage?
  • Data Flow Through a Blockchain
  • Blockchain Data Storage Solutions
  • Why Data Storage is Shifting to the Blockchain?
  • Issues with Centralised Data Centres

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Who should attend this Data Science and Blockchain Training Course?

The Data Science and Blockchain Training Course is designed for those who want to get better at understanding the intricacies surrounding Data Science and Blockchain for complex problem-solving and application development. The Data Science and Blockchain Training Course can benefit individuals such as:

  • Data Scientists
  • Blockchain Developers
  • Business Analysts
  • Software Engineers
  • Project Managers
  • Supply Chain Professionals
  • Ethical Hackers   

Prerequisites of the Data Science and Blockchain Training Course

There are no formal prerequisites for this Data Science and Blockchain Training Course.

Data Science and Blockchain Training Course Overview

Data Science stands as the discipline dedicated to extracting valuable insights from data to inform business decisions, strategic planning, and a myriad of other purposes. It relies on cutting-edge analytical techniques and scientific principles to achieve its objectives. In parallel, blockchain technology emerges as a digital ledger system for transactions, fortified by encryption and digital signatures that ensure their integrity and authenticity.

Mastery of Data Science and Blockchain is vital for Technology, Data Science, and Blockchain Development Professionals. Proficiency in these domains is particularly valuable in the dynamic landscape of data-driven industries. It empowers professionals to contribute innovatively, making it a crucial skill set for career growth and leadership roles. Integrating Data Science and Blockchain expertise positions individuals at the front of technological advancements and industry demands.

The 1-day Data Science and Blockchain Training Course is meticulously crafted to provide delegates with a deep understanding of Blockchain's pivotal role in empowering Data Scientists to address a wide array of intriguing challenges. Throughout this training course, delegates will delve into the roles and responsibilities integral to a Data Scientist's domain, mastering various modelling techniques to unlock Blockchain's solutions and applications.

Course Objectives

  • To learn how to alter information about the records retrospectively
  • To analyse data and track transactions to make better decisions
  • To understand the entire process of gathering actionable insights from raw data
  • To speed up the work process and reduces the time taken to obtain and analyse data
  • To identify dangerous or fraudulent transactions and prevent fraud entirely
  • To identify trends, models, and threats through data production and exchange

Upon completion of this 1-day course, delegates will gain a deep understanding of the synergies between data science and blockchain technology. They will be equipped with practical skills to navigate complex data segments, predict outcomes, and unlock blockchain's solutions.

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What’s included in this Introduction to Data Science and Blockchain Training Course?

  • World-Class Training Sessions from Experienced Instructors
  • Data Science and Blockchain 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 +32 35001305 or Enquire.

Data Science Courses FAQs

Data Science is a field that involves extracting insights and knowledge from data through various techniques, including statistics, machine learning, and data analysis. It encompasses the process of collecting, cleaning, and interpreting data to inform decision-making and solve complex problems.
These Data Science Courses provides valuable skills in data analysis, enhancing job prospects across industries. It enables data-driven decision-making and offers high-demand career opportunities with competitive salaries.
While related, Data Analytics primarily deals with analysing data for insights and decision-making. Data Science encompasses various disciplines, including statistics and machine learning, to gain deeper insights and solve complex problems using data.
While the prerequisites for attending our Data Science Training differ from one course to another, most of the courses do not have any formal prerequisites. However, a strong foundation in mathematics and statistics, skills in Python or R programming, and familiarity with SQL would immensely benefit the delegates.
Yes. The Knowledge Academy offers 24/7 support for delegates before, during, and after these Data Science Courses.
The duration of each Data Science Course Online varies. Please refer to your preferred course’s Dates and Prices section for specific information.
The Knowledge Academy provides flexible self-paced training for these Online Data Science Courses. Self-paced training is beneficial for individuals who have an independent learning style and wish to study at their own pace and convenience.
After completing these Data Science Courses, you can pursue a career as a Data Analyst, Data Engineer, Machine Learning Engineer, Data Scientist and Data Architect. Learn Data Science Certification Courses to gain a competitive edge in the job market.
Yes, we provide corporate training for these Data Science Courses, tailored to fit your organisation’s requirements.
In these courses, you'll learn data manipulation, statistical analysis, machine learning, data visualisation, big data technologies, Python/R programming, experimental design, ethical considerations, and real-world applications.
These Data Science Courses are suitable for aspiring Data Scientists, Data Analysts, Business Analysts, Software Engineers, and professionals from diverse industries keen on leveraging data for insights and advancing their careers in data-driven roles.
Should you encounter any difficulties accessing course materials, our customer support team is available to assist and promptly resolve any issues you may encounter.
The Knowledge Academy is the Leading global training provider for Data Science Courses.
The price for Data Science Courses certification in Belgium starts from €3495.

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