R Programming Hands-on Specialization for Data Science (Lv1)

Course Curriculum

Chapter 1: Introduction

Lecture 1: Warm Welcome!

Lecture 2: Why you should learn R?

Lecture 3: What you will learn in this course?

Chapter 2: R Fundamentals

Lecture 1: Installing R (console) and RStudio (IDE)

Lecture 2: Getting to know R – Setting Context

Lecture 3: R Basics – Working Directory, Environment Variables and more!

Lecture 4: R Basics – Loading and Executing R scripts from local file system

Chapter 3: R Data Types

Lecture 1: R Atomic Data Types Intro – What you must know about Numeric and Integers in R?

Lecture 2: Complex and Character Data Types (Atomic)

Lecture 3: Character Data Type (Atomic) + Important Data Transformation Functions (1)

Lecture 4: Character Data Type (Atomic) + Important Data Transformation Functions (2)

Lecture 5: Character Data Type (Atomic) + Important Data Transformation Functions (3)

Lecture 6: Logical Data Type (Atomic) and Its known Implications

Lecture 7: Atomic Data Types and Nuances in Coercioning (Explicit/Implicit)

Chapter 4: R Data Structure – Vectors

Lecture 1: Vectors – Creation, Homogeneity, Coercion Implications and Important Functions!

Lecture 2: Vectors – Comparing different ways to create vectors in R!

Lecture 3: Vectors – Understanding Indexing like never before!

Lecture 4: Vectors – Indexing (Out of Bound scenarios) and How Pros use it!

Lecture 5: Vectors – Flatness property and its critical implications in Indexing!

Lecture 6: Vectors – Labels and their Advanced Usage in Indexing

Lecture 7: Vectors – Assigning Attributes and its use-case as Metadata

Chapter 5: R Data Structure – Matrices

Lecture 1: Matrices – Getting Acquainted, Creation and its operational functions!

Lecture 2: Matrices – Creation and Implications related to its Dimensions

Lecture 3: Matrices – Creation from Vectors + Naming Dimensions (Explicit, Implicit)

Lecture 4: Matrices – Dimensions (Advanced) and Intro to Indexing

Lecture 5: Matrices – Indexing Continued

Lecture 6: Matrices – Advanced Indexing using DimensionNames

Lecture 7: Matrices – Even more Advanced Indexing!

Lecture 8: Matrices – Operations!

Chapter 6: R Data Structure – Lists

Lecture 1: Lists – Getting Introduced to one of the most powerful data structures in R

Lecture 2: Lists – Comparing with Vectors w.r.t Heterogeneity and Introducing Indexing

Lecture 3: Lists – Comprehending their Recursive Nature in comparison with Vectors

Lecture 4: Lists – Converting to and from Vectors and implications (coercion, flatness)

Lecture 5: Lists – Nuances in Determining Length in the context of Recursiveness

Lecture 6: Lists – Nuances in Determining Length and Class of Elements

Lecture 7: List – Advanced Indexing also using Labels

Lecture 8: List – Comparison of Indexing ways and Implications

Chapter 7: R Data Structure – Data Frames

Lecture 1: Data Frames – Introducing The holy grail of processing Structured Data

Lecture 2: Data Frames – Creation and important functions for Basic Exploratory Analysis

Lecture 3: Data Frames – More Important Functions for Basic Exploratory Analysis

Lecture 4: Data Frames – Creation from Lists

Lecture 5: Data Frames – Creation from Lists, Matrices and Vectors

Lecture 6: Data Frames – Everything you need to know about Subsetting

Lecture 7: Data Frames – Handling Missing Values like Pros!

Lecture 8: Data Frames – Imputing Missing Values like Pros!

Lecture 9: Data Frames – Advanced Subsetting Techniques for robust analytics

Chapter 8: R Control Structures

Lecture 1: While Loops in R

Lecture 2: For Loops in R – Intro and Practical Use-Cases

Lecture 3: If Else Structures in R

Lecture 4: If Else Structures in R (2)

Lecture 5: If Else Structures in R (3)

Chapter 9: Data Science Application in R – Automated Web Scraping Bot

Lecture 1: Web Scraping – Setting Context + Highlighting Use-Cases

Lecture 2: Web Scraping – One Simple yet Powerful Way to do so!

Lecture 3: Web Scraping – Use Case: Custom Churn Analysis

Lecture 4: Use Case: Custom Churn – Performing Data Munging and Transformations

Lecture 5: Use Case: Custom Churn – Performing Data Munging and Transformations

Lecture 6: Use Case: Custom Churn – Performing Data Cleansing

Lecture 7: Web Scraping – Contextual understanding of HTML

Lecture 8: Web Scraping – Contextual Understanding of HTML Tags

Lecture 9: Web Scraping – How to exploit the Structure of Web Page for Efficient Scraping

Lecture 10: Web Scraping – Contextual Understanding of HTML Document Object Model (DOM)

Lecture 11: Web Scraping on Steroids – XPath in R!

Lecture 12: Web Scraping on Steroids – XPath in R (2)

Lecture 13: Web Scraping using XPath – Programmatic Extraction of Data from HTML Tags

Lecture 14: Web Scraping using XPath – Programmatic Extraction of Data from HTML Tags (2)

Lecture 15: Automating Web Scraping – RSelenium!

Lecture 16: Automated Web Scraping – Contextual Understanding of Selenium Components

Lecture 17: Automated Web Scraping – installing RSelenium in R

Lecture 18: Automated Web Scraping – Initialising RSelenium Server

Lecture 19: Automated Web Scraping – Connecting to RSelenium Server using Reference Class

Lecture 20: Automated Web Scraping – Navigating and Sending Key Strokes in Web Pages

Lecture 21: Web Scraping Use Case Context Setting

Lecture 22: Web Scraping Pipeline – Deep dive of workflow pattern

Lecture 23: Systematic analysis of website for efficient Scraping

Lecture 24: Installing and Loading RSelenium

Lecture 25: Starting Selenium Server – The right way!

Lecture 26: Handling RSelenium's Driver Issues

Lecture 27: Launching Selenium Server jar with correct driver settings (part 2)

Lecture 28: Web Scraper Program Initialisation and Remote Driver Object Instantiation

Lecture 29: Navigating web pages using RSelenium and Using Xpath for data extraction

Lecture 30: Using R's Apply Family of Functions for Data Extraction from RSelenium Objects

Lecture 31: Advanced Data Munging using R Regex and String Processing Functions

Lecture 32: Advanced Data Munging using R Regex and String processing functions (II)

Lecture 33: Advanced Data Munging – Discretizing Continuous Values

Lecture 34: Advanced Data Frames Manipulation

Lecture 35: Orchestrating Automation of Web Scraping Routine

Lecture 36: Advanced Statistical Analysis and Visualisation for Informed Decision Making