Certificate Program in Data Science & Advanced Machine Learning using R
Learn concepts of data analytics, data science and advanced machine learning using R with hands-on case studies
Course Description
This is an advanced course by Imurgence using R which dives deep into data analytics, R interface, and data handling by thoroughly understanding data structure and data types in R, R internal functions, data with manipulation and visualisation, basic statistics, probability, inferential statistics, linear and logistic regression, decision tree, ensemble learning, support vector machines, market basket analysis, k-nearest neighbours, clustering and artificial neural network.
Course Outcomes:
- Upon successful completion of this course, become skilled in data science and machine learning using R for predictive analytics on data and use machine learning to solve problems.
Course Details:
Target Audience
This course is ideal for anyone looking to improve their skills or start a career in data science, business analytics, artificial intelligence (AI) or machine learning.
Access Timeframe
Three months from the date of access.
Prerequisites
There are no prerequisites to this course, but a general understanding of statistics and an inclination to learn coding would benefit the learner.
Certificate Info:
Type of Certification
Certificate of Completion
Format of Certification
Digital
Professional Association/Affiliation
The certificate is issued by Imurgence an autonomous institution.
Method of Obtaining Certification
Upon successful completion of this course, the learner will be sent a digital copy of the certificate to their email.
Course Outline
In this module, you will learn the following:
- Overview Analytics
- Application of Analytics
In this module, you will learn the following:
- Installation of R Base
- Installation of R Studio
- How to Use R Studio
In this module, you will learn the following:
- Data Structures
- Data Types
- Basic Operations in R
In this module, you will learn the following:
- Decision Making
- Loops
In this module, you will learn the following:
- Built-in Functions
- User Defined Functions
In this module, you will learn the following:
- Import and Export of Data
- Subsetting
- Merge and Concatenate
In this module, you will learn the following:
- Basic Plots
In this module, you will learn the following:
- Descriptive Statistics
In this module, you will learn the following:
- Basic Definitions
- Basic Rules for Probability
- Baye's Theorem
In this module, you will learn the following:
- Hypothesis
- Hypothesis Testing
- Correlation
In this module, you will learn the following:
- Simple Linear Regression
- Multiple Linear Regression
- Regularization
In this module, you will learn the following:
- Basics
- Logistic Regression Theory
- Implementation in R
In this module, you will learn the following:
- Theory
- Practical
In this module, you will learn the following:
- Introduction
- Practical
In this module, you will learn the following:
- Theory
- Practical
In this module, you will learn the following:
- Theory
- Practical
In this module, you will learn the following:
- Theory
- Practical
In this module, you will learn the following:
- Theory
- Practical
In this module, you will learn the following:
- Theory
- Practical
In this module, you will learn the following:
- Why Visualisation? Why Tableau? Things you should know about Tableau
In this module, you will learn the following:
- 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
In this module, you will learn the following:
- Joining Multiple Tables
- Copy and Paste
- Data Extracts
In this module, you will learn the following:
- 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
In this module, you will learn the following:
- 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
In this module, you will learn the following:
- Sorting and Grouping
- Aliases
- Filtering and Quick Filters
- Totals and Subtotals
- Aggregation and Disaggregation
- Percent of Total
- Working with Statistics and Trendlines
In this module, you will learn the following:
- Working with String Functions
- Basic Arithmetic Calculations
- Date Math
- Working with Totals
- Custom Aggregations
- Logic Statements
In this module, you will learn the following:
- Options in Formatting Your Visualisation
- Working with Labels and Annotations
- Effective Use of Titles and Captions
- Introduction to Visual Best Practices
In this module, you will learn the following:
- Theory
- Understanding the Buying Pattern of a Customer
In this module, you will learn the following:
- Downloading Shapes from the Internet
- Making Use of Customized Shapes
In this module, you will learn the following:
- Combining Multiple Visualisations into a Dashboard
- Making Your Worksheet Interactive by Using Actions and Filters
- An Introduction to Best Practices in Visualisation
In this module, you will learn the following:
- Publish to Reader
- Packaged Workbooks
- Publish to Office
- Publish to PDF
- Publish to Tableau Server and Sharing over the Web
