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Introduction of Python
Working with IPython
Introduction to NumPy
Working with Pandas
Visualisation with Matplotlib
There are no prerequisites for attending this course. However, a basic understanding of programming would be beneficial.
Anyone interested in having a career in Python can attend this course. This course is well-suited for:
Python is a premier and powerful open-source language that is easy to use and has powerful libraries for data manipulation and analysis. It is a multi-paradigm programming language and supports object-oriented programming, functional programming patterns, and structured programming. This Python Data Science Training is designed to equip delegates with the knowledge of programming language for the domain of data science.
In this 3-day training, delegates will learn how to create arrays from scratch and python lists. Delegates will acquire a comprehensive knowledge of data manipulation with pandas. In addition, they will learn how to rearrange multi-indices, combine datasets, and work with time series. Delegates will get an understanding of simple line plots and simple scatter plots.
During this course, delegates will gain in-depth knowledge of how to visualise a three-dimensional function. Furthermore, familiarise yourself with histograms, binnings, and density. Delegates will learn how to customise plot legends and colorbars. Post completion of this training, delegates shall be able to customise matplotlib as well.
Module 1: Python for Data Analysis - NumPy
Module 2: Python for Data Analysis – Pandas
Module 3: Python for Data Visualisation – Matplotlib
Module 4: Python for Data Visualisation – Seaborn
Capstone 1: Retrieving, Processing and Visualising Data with Python
Module 5: Machine Learning
Module 6: Natural Language Processing
Module 7: Deep Learning
Module 8: Big Data
Capstone 2: Machine Learning Applications in Retail, Hospitality, Education and Insurance Sectors
Module 9: Working with Data in R
Module 10: Regression in R
Capstone 3: Retrieving, Processing and Visualising Data with R
Module 11: Modelling Data in Power BI
Module 12: Shaping and Combining Data using Power BI
Module 13: Interactive Data Visualisations
Capstone 4: Product- Sales Analysis using Power BI
There are no formal prerequisites for attending this Advanced Data Science Certification. However, having a prior knowledge of programming languages will be beneficial for the delegates.
This Advanced Data Science Certification is suitable for anyone who wants to take their skills to the next level and add-on into their existing skillset. However, it is much more beneficial for:
The Knowledge Academy’s 4-day Advanced Data Science Certification provide delegates with a comprehensive knowledge of basic to advanced concepts to make a Data Scientist. Delegates will learn various concepts such as NumPy arrays, installing Pandas, object-oriented interface, regression analysis, machine learning mathematics, etc. Our highly experienced and professional trainers will conduct this training who have years’ of experience in teaching Data Science training courses.
Apart from these, delegates will learn the following essential concepts, such as:
After attending this expert training, delegates will be able to operate on the data in Pandas and working with time series. They will also be able to shape and combine data using Power BI successfully and implement interactive data visualisations.
The following modules will be covered during this Probability and Statistics for Data Science Course:
Basic Probability Theory
Multivariate Random Variables
The convergence of Random Processes
There are no formal prerequisites for attending this course.
Anyone interested in learning how to apply probability and statistics to data science can attend this course.
Probability is the most fundamental skill required to be successful in the business world. This Probability and Statistics for Data Science training course is designed to acquaint delegates with the most fundamental concepts in the field of probability. The course will equip delegates with the knowledge about probability and statistics to tackle the problems related to business and data science.
The Knowledge Academy’s Probability and Statistics for Data Science training is crafted to equip delegates with a comprehensive understanding of complicated probabilistic concepts. This course will take your career to the next level, which is of probability, Bayesian probability, conditional probability, and probability distributions.
During this 2-day course, delegates will learn about discrete and continuous random variables. The course will teach delegates how to generate multivariate random variables. In addition, delegates will gain knowledge gaussian and poisson process. Post completion of this training, delegates will become familiarised with parametric and nonparametric testing.
Introduction to Text Mining
Core Text Mining Operations
Text Mining Preprocessing Techniques
Introduction to Clustering
Information Extraction (IE)
Probabilistic Models for IE
Introduction to Link Analysis
There are no prerequisites for attending this course.
Anyone wishes to develop their knowledge and skill-set can attend this course. This course is well-suited for:
Text mining is a knowledge-intensive process in which a user interacts with a document collection over time with the help of a suite of analysis tools. A document collection can be any grouping of text-based documents. Text Mining seeks to extract valuable information from data sources by identifying and exploring patterns.
This course is designed to provide complete knowledge of text mining operations and preprocessing techniques. Delegates will get an understanding of the text categorisation problem. They will learn about significant algorithms to perform text categorisation. Also, they will learn how to use unlabelled data and evaluate text classifiers.
During this 2-day training, delegates will be equipped with the knowledge of clustering and Information Extraction (IE). Delegates will learn how to access constraints and simple specification filters at the presentation layer. Then, delegates will be introduced to hidden Markov models and maximal entropy Markov models. Post completion of this training, delegates will be able to use MEMM for Information Extraction.
This Introduction to Keras Training will explore the following topics:
Introduction to Keras
Overview of Keras Layers
There are no formal prerequisites for attending this course.
Anyone interested in learning about Keras can attend this two-day intensive course. This course is well-suited for:
Keras is an open-source neural network library written in Python and capable of running on top of CNTK, TensorFlow, or Theano. Keras was developed to enable fast experimentation and is extensively used by data scientists to architect the neural network for complex problems. Keras can serve as higher-level API, which means it can act as an interface for Theano, TensorFlow, etc. Keras also compiles model with loss and optimiser functions, training process with fit function. It does not handle low-level API such as making the computational graph, making tensors or other variables as the backend engine has dealt with it.
This 1-day Introduction to Keras Training is designed to provide knowledge to delegates about Keras and the usage of Keras. Delegates will learn about different Keras layers such as core layers, convolutional layers, pooling layers, locally-connected layers, recurrent layers, etc. In addition, delegates will learn how to perform sequence, text, and image preprocessing. Post completion of this intensive training, delegates will be able to use regularisers and constraints.
The pandas: A Python Data Analysis Toolkit is a two-day course. The following is a brief synopsis of the topics that will be covered in this course.
Introduction and Installation
Getting Started with pandas
There are no prerequisites to attend this course. However, a basic knowledge of programming would be beneficial.
Anyone wishes to develop their knowledge and skillset on python libraries can attend this course. This course is beneficial for those who want to make a career in data science and data analytics.
Pandas is an open-source python library that provides high-performance, data analysis and data structures tool for the Python programming language. Python with pandas can be used in numerous fields, such as statistics, economics, and analytics. This course is designed to provide knowledge of how to quickly and easily analyse data with Python’s powerful library- pandas.
In this 2-day course, delegates will gain comprehensive knowledge of pandas and data structures. Delegates will learn how to work with text data, missing data, and categorical data. In addition, they will get an understanding of merge, join, concatenate, reshaping and pivot tables.
During this course, delegates will become familiarised with visualisation, IDE, data validation, and extension data types. Delegates will learn how to store pandas DataFrame objects in Apache Parquet format. On completion of this course, delegates will get an understanding of various themes in pandas.
This Predictive Analytics Training course will explore the following areas:
Introduction to Predictive Analytics
Setting Up the Problem
Understanding the Data
Itemsets and Association Rules
Interpreting Descriptive Models
Predictive Models Assessment
There are no formal prerequisites for attending this course.
Anyone who needs to gain knowledge on predictive analytics can attend this course. This course is well-suited for data analysts and data scientists.
Predictive analytics is used for making predictions about unknown future events. It makes use of many techniques, including data mining, modelling, statistics, artificial intelligence, and machine learning by analysing current data. The patterns found in transactional and historical data can be used for identifying future risks and opportunities. Predictive analytics models capture relationships among factors for evaluating risk with a specific set of conditions for assigning a score. Predictive analytics enables organisations to become forward-looking and proactive, predicting outcomes and behaviours based on the data.
This Predictive Analytics Training course will provide delegates with the knowledge of predictive analytics and its processing steps. Delegates will become familiarised with cleaning and feature creation. This 2-day course will equip delegates with extensive knowledge of itemsets and association rules. Delegates will also be familiarised with the various predictive modelling techniques including logistic regression, k-nearest neighbour, Naïve Bayes, and more.
Installing Knime Analytics Platform
Introduction to Knime Analytics Platform
Exploring Knime Workbench
Knime Extensions and Integrations
Creating New Knime Extension
There are no prerequisites to attend this course.
Anyone who wishes to learn about the basic functionalities of the Knime analytics platform can attend this course.
Knime Analytics Platform is an open-source software to create data science applications and services. With the help of Knime, understanding data, and designing data science workflows and reusable components is accessible to everyone. Knime analytics platform allows you to create visual workflows with an intuitive, drag and drop style graphical interface without any need of coding.
In this 1-day training course, delegates will learn how to install and update the Knime Analytics Platform and extensions. Delegates will gain knowledge of Knime workbench and Knime tables. In addition, they will get an understanding of Knime extensions and integrations.
During this training course, delegates will be equipped with knowledge of community and partner extensions. Delegates will learn how to create new Knime extension project. By the end of this training, delegates will be able to set the Knime SDK and deploy extension.
Getting Started with Data Mining
Data Warehousing and Online Analytical Processing
Mining Frequent Patterns, Associations, and Correlations
Advanced Pattern Mining
Advanced Methods of Classification
Advanced Cluster Analysis
There are no formal prerequisites for attending this course. However, basic knowledge of the IT industry will be beneficial.
Anyone who is interested in learning the data mining can attend this course. This course is best-suited for IT managers aiming to improve data management and analysis techniques.
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 Mining Training 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 discretization. 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.
Introduction to Geographic Information Systems (GIS)
Basics of ArcGIS
Making Maps With Common Datasets
Retrieving and Sharing Data
There are no formal prerequisites for attending this course.
Anyone interested to have a fundamental knowledge of GIS can attend this course.
The GIS (Geographic Information System) is a framework to gather, manage, and analyse data. GIS integrates several types of data and analyses spatial location as well as organises layers of information into visualisations using maps and 3Dscenes. GIS comes down to just the following for simple ideas:
The Knowledge Academy’s GIS Development Training course is designed to provide a comprehensive knowledge of the fundamentals of geographic information systems. This course will explore the world of spatial analysis and cartography with GIS. Delegates will learn the basics of ArcGIS – the leading software tool. Delegates will get an understanding of how GIS has been developed from paper maps to the globally integrated electronic software packages.
During this 1-day course, delegates will gain knowledge of how to analyse data with geoprocessing tools. In addition, they will learn about core map elements and symbology. Our expert instructors will equip you with the extensive knowledge making the course the best it can be. Delegates will learn how to create and use map packages. Furthermore, they will also get an understanding of how to upload packages to ArcGIS online. Post completion of this training, delegates will be able to create layer files and packages.
Decision Tree Modeling Using R Training Course Outline
Introduction to Decision Tree
Data Design for Modelling
Data Treatment before Modelling
Classification of Tree development and Algorithm details
Industry Practice of Classification Tree - Development, Validation and Usage
Regression Tree and Auto Pruning
Basic knowledge of R programming language required before attending this course.
This training course is ideal for anyone; however, Professionals and Students who want to enter the Analytics Industry. This course is also ideal for Analytics Professionals and Data Mining Professionals.
Decision Tree Modeling Using R Training Course Overview
Decision Tree Modeling Using R is a popular Analytic technique which can be implemented in various business fields such as money lending business, automobile, and telecom.
This 1-day Decision Tree Modeling Using R Certification course is designed to provide delegates with a solid understanding of various concepts such as Data treatment before modelling frequency distribution, the algorithm behind decision tree, how is a decision tree developed, GINI method, steps of pruning, ID3, random forest method, and more. Starting from fundamentals of Decision Tree delegates will learn other advance topics such as data design for modelling, data treatment before modelling, classification of tree development and algorithm details, industry practice of classification tree - development, validation and usage, understand K fold validation for the model, CHAID Algorithm, the syntax for CHAID using R, and CHAID vs CART, using R for Random forest method and more.
After attending this course, delegates will gain expertise in Decision Tree Modeling using the R programming language.
Module 1: Introduction to PySpark
Module 2: Installation
Module 3: DataFrame
Module 4: Setting Up a Spark Virtual Environment
Module 5: Building Batch and Streaming Apps with Spark
Module 6: Learning from Data Using Spark
In this PySpark Training course, there are no formal prerequisites.
This PySpark Training provided by The Knowledge Academy is ideal for anyone who wants to learn the use of PySpark to support the collaboration of Apache Spark and Python.
PySpark is an interface for Apache Spark in Python and a comprehensive language for conducting exploratory data analysis at scale, for creating machine learning pipelines and building ETLs for a data platform. PySpark supports various features of Spark like Spark SQL, DataFrame, Streaming, MLlib, and Spark Core. It comes with immense benefits to its users and organisations, including simple to write, the framework handles errors, various useful algorithms, etc. This PySpark Training is curated by industry experts to help individuals in mastering skills required by utilising PySpark features in their day-to-day tasks and get opportunities to work on lucrative job posts in multinational companies.
In this 1-day PySpark Training course, delegates will learn about using the Conda environment to export their third-party Python packages by leveraging Conda-pack. They will gain in-depth knowledge about using virtualenv to manage Python dependencies in their clusters by using venv-pack. Further, delegates will learn other crucial concepts, such as reading, writing, and transforming data, MLlib, using PyPI, Conda, PySpark native features, Virtualenv, and PEX, connecting to network servers, etc. Our expert and technically sound trainer, who has years of experience in teaching technical courses, will conduct this training.
This training course will cover various essential concepts, such as:
After attending this training course, delegates will be able to use conceptual frameworks for implementing the architecture of data-intensive applications in their organisations. They will also be able to harvest the data, ensuring its integrity and preparing for batch and streaming data processing by Spark.
Speak to a training expert for advice if you are unsure of what course is right for you. Give us a call on +1 6474932992 or Inquire.
Course was run very smoothly, Richard our trainer was extremely knowledgeable and delivered the course in a succinct fashion with a twist of humour thrown in.
The course was great and the so was the trainer - brilliantly delivered and I would certainly recommend this course to my colleagues. Thanks to Richard for a great course and delivering it a tough environment virtually.
Richard was very knowledgeable and explained well
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