Projects in Data Science Using R

Course Curriculum

Chapter 1: Introduction to Data Science Using R

Lecture 1: Introduction

Lecture 2: Intro to R studio

Lecture 3: The Assignment Operator

Lecture 4: Basic Data Types in R

Lecture 5: Vectors

Lecture 6: Matrices and Data Frames

Lecture 7: Subsetting Syntax

Lecture 8: Project 1 : Introduction to R – Problem Statement

Lecture 9: Project 1 Solution

Chapter 2: Data Transformation

Lecture 1: Data Transformations on Rows

Lecture 2: Data Transformations on Columns

Lecture 3: Data Transformations on Iris Dataset – Project Problem Statement

Lecture 4: Data Transformations on Iris Dataset – Project Solution

Lecture 5: Wide and Long Data

Lecture 6: Grouped Transposes

Lecture 7: Project 2 : Wide and Long Data – Problem statement

Lecture 8: Project 2 Solution

Lecture 9: What are Joins

Lecture 10: Programming Joins Part 1

Lecture 11: Programming Joins Part 2

Lecture 12: Project 3 :Performing Joins – Problem Statement

Lecture 13: Project 3 Solution

Chapter 3: Data Visualization

Lecture 1: GGPLOT Basics

Lecture 2: Aesthetic Mappings in GGPLOT

Lecture 3: Facets in GGPLOT

Lecture 4: Geoms in GGPLOT

Lecture 5: Statistical Transformations in GGPLOT

Lecture 6: Project 4 : GGPLOT – Problem Statement

Lecture 7: Project 4 Solution

Lecture 8: Project 5: Facets, Geoms and Tansformations

Lecture 9: Project 5 Solution

Chapter 4: Exploratory Data Analysis

Lecture 1: How to Identify Missing Values

Lecture 2: How to Identify Outliers

Lecture 3: What to do with Missing Values and Outliers

Lecture 4: Functional Transformations

Chapter 5: Regression Models

Lecture 1: Intro to Regression Problem and Data Set

Lecture 2: Exploratory Data Analysis

Lecture 3: Correlations and Final Data Set

Lecture 4: What is Multiple Regression

Lecture 5: Building a Multiple Regression Model

Lecture 6: Measuring Regression Model Accuracy

Chapter 6: KNN Model

Lecture 1: What is KNN

Lecture 2: Building a KNN Model

Lecture 3: Assessing KNN Model Performance

Lecture 4: Assessing Training and Test Error for KNN

Lecture 5: What is a Decision Tree

Lecture 6: Creating a Decision Tree

Lecture 7: Assessing Performance of a Decision Tree

Lecture 8: Model Comparison

Lecture 9: Project: Build a model that is better than our multiple regression and KNN model

Chapter 7: Classification Dataset

Lecture 1: Intro to Classification Dataset and Problem

Lecture 2: EDA Part 1

Lecture 3: EDA Part 2

Lecture 4: What is Logistic Regression

Lecture 5: Building a Logistic Regression Model

Lecture 6: Building a Classification Tree

Lecture 7: Building a Random Forest

Lecture 8: Project: Build a model better than logistic regression, decision and RF model