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- Introduction to Course
- Course Curriculum
- What is Data Science?
- Course FAQ
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
₹ 4,099
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Course Introduction
Course Best Practices
- How to Get Help in the Course!
- Welcome to the Course.
- Installation and Set-Up
Windows Installation Setup
- Windows Installation Procedure
Mac OS Installation Setup
- Mac OS Installation Procedure
Linux Installation
- Linux/Unbuntu Installation Procedure
Development Environment Overview
- Development Environment Overview
- Course Notes
- Guide to RStudio
Introduction to R Basics
- Introduction to R Basics
- Arithmetic in R
- Variables
- R Basic Data Types
- Vector Basics
- Vector Operations
- Comparison Operators
- Vector Indexing and Slicing
- Getting Help with R and RStudio
- R Basics Training Exercise
- R Basics Training Exercise - Solutions Walkthrough
R Matrices
- Introduction to R Matrices
- Creating a Matrix
- Matrix Arithmetic
- Matrix Operations
- Matrix Selection and Indexing
- Factor and Categorical Matrices
- Matrix Training Exercise
- Matrix Training Exercises - Solutions Walkthrough
R Data Frames
- Introduction to R Data Frames
- Data Frame Basics
- Data Frame Indexing and Selection
- Overview of Data Frame Operations - Part 1
- Overview of Data Frame Operations - Part 2
- Data Frame Training Exercise
- Data Frame Training Exercises - Solutions Walkthrough
R Lists
- List Basics
Data Input and Output with R
- Introduction to Data Input and Output with R
- CSV Files with R
- Note on R with Excel Download
- Excel Files with R
- SQL with R
- Web Scraping with R
R Programming Basics
- Introduction to Programming Basics
- Logical Operators
- if, else, and else if Statements
- Conditional Statements Training Exercise
- Conditional Statements Training Exercise - Solutions Walkthrough
- While Loops
- For Loops
- Functions
- Functions Training Exercise
- Functions Training Exercise - Solutions
Advanced R Programming
- Introduction to Advanced R Programming
- Built-in R Features
- Apply
- Math Functions with R
- Regular Expressions
- Dates and Timestamps
Data Manipulation with R
- Data Manipulation Overview
- Guide to Using Dplyr
- Guide to Using Dplyr - Part 2
- Pipe Operator
- Quick note on Dpylr exercise
- Dplyr Training Exercise
- Dplyr Training Exercise - Solutions Walkthrough
- Guide to Using Tidyr
Data Visualization with R
- Overview of ggplot2
- Histograms
- Scatterplots
- Barplots
- Boxplots
- 2 Variable Plotting
- Coordinates and Faceting
- Themes
- ggplot2 Exercises
- ggplot2 Exercise Solutions
Data Visualization Project
- Data Visualization Project
- Data Visualization Project - Solutions Walkthrough - Part 1
- Data Visualization Project Solutions Walkthrough - Part 2
Interactive Visualizations with Plotty
- Overview of Plotly and Interactive Visualizations
- Resources for Plotly and ggplot2
Capstone Data Project
- Introduction to Capstone Project
- Capstone Project Solutions Walkthrough
Introduction to Machine Learning with R
- ISLR PDF
- Introduction to Machine Learning
Machine Learning with R - Linear Regression
- Introduction to Linear Regression
- Linear Regression with R - Part 1
- Linear Regression with R - Part 2
- Linear Regression with R - Part 3
Machine Learning Project - Linear Regression
- Introduction to Linear Regression Project
- ML - Linear Regression Project - Solutions Part 1
- ML - Linear Regression Project - Solutions Part 2
Machine Learning with R - Logistic Regression
- Introduction to Logistic Regression
- Logistic Regression with R - Part 1
- Logistic Regression with R - Part 2
Machine Learning Project - Logistic Regression
- Introduction to Logistic Regression Project
- Logistic Regression Project Solutions - Part 1
- Logistic Regression Project Solutions - Part 2
- Logistic Regression Project - Solutions Part 3
Machine Learning with R - K Nearest Neighbors
- Introduction to K Nearest Neighbors
- K Nearest Neighbors with R
Machine Learning Project - K Nearest Neighbors
- Introduction K Nearest Neighbors Project
- K Nearest Neighbors Project Solutions
Machine Learning with R - Decision Trees and Random Forests
- Introduction to Tree Methods
- Decision Trees and Random Forests with R
Machine Learning Project - Decision Trees and Random Forests
- Introduction to Decision Trees and Random Forests Project
- Tree Methods Project Solutions - Part 1
- Tree Methods Project Solutions - Part 2
Machine Learning with R - Support Vector Machines
- Introduction to Support Vector Machines
- Support Vector Machines with R
Machine Learning Project - Support Vector Machines
- Introduction to SVM Project
- Support Vector Machines Project - Solutions Part 1
- Support Vector Machines Project - Solutions Part 2
Machine Learning with R - K-Means Clustering
- Introduction to K-Means Clustering
- K Means Clustering with R
Machine Learning Project - K-Means Clustering
- Introduction to K Means Clustering Project
- K Means Clustering Project - Solutions Walkthrough
Machine Learning with R - Natural Language Processing
- Introduction to Natural Language Processing
- Natural Language Processing with R - Part 1
- Natural Language Processing with R - Part 2
Machine Learning with R - Neural Nets
- Introduction to Neural Nets
- Neural Nets with R
Machine Learning Project - Neural Nets
- Introduction to Neural Nets Project
- Neural Nets Project - Solutions
Bonus Section
- Bonus Lecture
Instructors
Articles
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