Certificate Program in Data Science & Advanced Machine Learning using R & Python
Learn concepts of data analytics, data science and advanced machine learning using R and Python with hands-on case studies

Course Description
This is a comprehensive Imurgence course using R and Python which dives deep into data analytics, R interface, data handling by thoroughly understanding data structure and data types in R, R internal functions, data with manipulation and visualisation, basic statistics, probability, inferential, linear and logistic regression, decision tree, ensemble learning, support vector machines, market basket analysis, k-nearest neighbours, clustering, artificial neural network, introduction to data analytics, Python IDE, Python basics, Python packages, and linear and logistic regression.
- Upon successful completion of this course, the learner will be skilled in data science and machine learning using R and Python for predictive analytics on data and use machine learning to solve problems.
Target Audience
Access Timeframe
Prerequisites
Type of Certification
Format of Certification
Professional Association/Affiliation
Method of Obtaining Certification
Course Outline
- Overview Analytics
- Application of Analytics
- Installation of R Base
- Installation of R Studio
- How to Use R Studio
- Data Structures
- Data Types
- Basic Operations in R
- Decision Making
- Loops
- Built-in Functions
- User Defined Functions
- Import and Export of Data
- Subsetting
- Merge and Concatenate
- Basic Plots
- Descriptive Statistics
- Basic Definitions
- Basic Rules for Probability
- Baye's Theorem
- Hypothesis
- Hypothesis Testing
- Correlation
- Simple Linear Regression
- Multiple Linear Regression
- Regularization
- Basics
- Logistic Regression Theory
- Implementation in R
- Theory
- Practical
- Introduction
- Practical
- Theory
- Practical
- Theory
- Practical
- Theory
- Practical
- Theory
- Practical
- Theory
- Practical
- Why Visualisation? Why Tableau? Things you should know about Tableau
- Connecting to Data and Introduction to Data Source Concepts
- Understanding the Tableau Workspace
- Dimensions and Measures
- Tour of Shelves
- Building Basic Views
- Help Menu
- Saving and Sharing Your work
- Concepts and Options When Connecting to Data
- Joining Multiple Tables
- Copy and Paste
- Data Extracts
- Understand how to deal with data changes in your data source such as field addition, deletion or name change
- Re-using and sharing data connections - the same concept of meta data
- Working with multiple connections in the same workbook analysis
- Marks
- Size and Transparency
- Highlighting
- Working with Dates
- Dual Axis/Multiple Measures
- Combo Charts with Different Mark Types
- Geographic Map
- Heat Map
- Scatter Plots
- Pie Charts and Bar Charts
- Small Multiples
- Working with Aggregate Versus Disaggregate Data
- Sorting and Grouping
- Aliases
- Filtering and Quick Filters
- Totals and Subtotals
- Aggregation and Disaggregation
- Percent of Total
- Working with Statistics and Trendlines
- Working with String Functions
- Basic Arithmetic Calculations
- Date Math
- Working with Totals
- Custom Aggregations
- Logic Statements
- Options in Formatting Your Visualisation
- Working with Labels and Annotations
- Effective Use of Titles and Captions
- Introduction to Visual Best Practices
- Theory
- Understanding the Buying Pattern of a Customer
- Downloading Shapes from the Internet
- Making Use of Customized Shapes
- Combining Multiple Visualisations into a Dashboard
- Making Your Worksheet Interactive by Using Actions and Filters
- An Introduction to Best Practices in Visualisation
- Publish to Reader
- Packaged Workbooks
- Publish to Office
- Publish to PDF
- Publish to Tableau Server and Sharing over the Web
- Overview of Analytics
- Application of Analytics
- Installation of Python
- How to Use Python
- Data Types and Data Structure
- Basic Operations in Python
- Functions
- Pandas
- Numpy
- Scikit-learn
- Matplotlib
- Descriptive Statistics
- Simple Linear Regression
- Multiple Linear Regression
- Regularization
- Basics
- Logistic Regression Theory
- Implementation in Python
- Theory
- Practical
- Introduction
- Practical
- Theory
- Practical
- Theory
- Practical
- Theory
- Practical
- Theory
- Practical
- Why Visualisation? Why Tableau? Things you should know about Tableau
- Connecting to Data and Introduction to Data Source Concepts
- Understanding the Tableau Workspace
- Dimensions and Measures
- Tour of Shelves
- Building Basic Views
- Help Menu
- Saving and Sharing Your work
- Concepts and Options When Connecting to Data
- Joining Multiple Tables
- Copy and Paste
- Data Extracts
- Understand how to deal with data changes in your data source such as field addition, deletion or name change
- Re-using and sharing data connections - the same concept of meta data
- Working with multiple connections in the same workbook analysis
- Marks
- Size and Transparency
- Highlighting
- Working with Dates
- Dual Axis/Multiple Measures
- Combo Charts with Different Mark Types
- Geographic Map
- Heat Map
- Scatter Plots
- Pie Charts and Bar Charts
- Small Multiples
- Working with Aggregate Versus Disaggregate Data
- Sorting and Grouping
- Aliases
- Filtering and Quick Filters
- Totals and Subtotals
- Aggregation and Disaggregation
- Percent of Total
- Working with Statistics and Trendlines
- Working with String Functions
- Basic Arithmetic Calculations
- Date Math
- Working with Totals
- Custom Aggregations
- Logic Statements
- Options in Formatting Your Visualisation
- Working with Labels and Annotations
- Effective Use of Titles and Captions
- Introduction to Visual Best Practices
- Theory
- Understanding the Buying Pattern of a Customer
- Downloading Shapes from the Internet
- Making Use of Customized Shapes
- Combining Multiple Visualisations into a Dashboard
- Making Your Worksheet Interactive by Using Actions and Filters
- An Introduction to Best Practices in Visualisation
- Publish to Reader
- Packaged Workbooks
- Publish to Office
- Publish to PDF
- Publish to Tableau Server and Sharing over the Web