Data Science with R – Beginners

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Data Science with R

Lecture 2: Understanding Datascience and its Modules

Chapter 2: Tools

Lecture 1: R Project for Statistical Computing

Lecture 2: Purpose of using R Tool

Lecture 3: Module on Data Visualization

Chapter 3: Creating Pie Charts and Bar Chart

Lecture 1: Creating Pie Charts

Lecture 2: Creating Bar Charts

Chapter 4: Creating Histograms

Lecture 1: Functions of Histogram

Lecture 2: Method of Using Scatterplots

Lecture 3: Creating Data for Line Charts

Chapter 5: Basic Data Visualization

Lecture 1: Case Study for Vector Values

Lecture 2: Module on Advanced Data Visualization

Chapter 6: Ggplot Value

Lecture 1: with Functions for Plotting Values

Lecture 2: How to Plot Car Value

Lecture 3: Understanding the Ggplot Value

Lecture 4: Basic Example on Scatterplot

Lecture 5: Scatterplot With Encircling

Lecture 6: Learning the Jitter Plot

Lecture 7: Counts Charts in Ggplot

Lecture 8: Section on Bubble Chart

Lecture 9: Diverging Bars with Ggplot

Lecture 10: Diverging Lollipchart with Ggplot

Lecture 11: Implementation of Dot Plot

Lecture 12: Purpose of using Area Charts

Lecture 13: Ordered Bar Chart for Multiple Items

Lecture 14: Simple Demonstration on Pie Chart

Lecture 15: Example on Hierarchical Dendrogram

Lecture 16: Learning about the Population Pyramids

Lecture 17: Understanding the Change Plot

Lecture 18: Case Study on Seasonal Plot

Lecture 19: Basic Understanding on Statistics

Chapter 7: Regression

Lecture 1: Implementation of Mean Median and Mode

Lecture 2: Understanding the Linear Regression

Lecture 3: Understanding Multiple Regression

Lecture 4: Functions of Logistic Regression

Lecture 5: Learning Normal Distribution Curve

Lecture 6: Understanding the Binomial Distribution

Lecture 7: Involvement of Poisson Regression

Lecture 8: Analysis of Covariance

Lecture 9: Time Series Analysis

Lecture 10: Nonlinear Least Square

Lecture 11: Section on Decision Tree

Lecture 12: The Random Forest Approach

Lecture 13: Learning the Chi Square Test

Lecture 14: Case Study on Survival Analysis

Chapter 8: Machine Learning and its Concepts

Lecture 1: Understanding the Concept of Probability

Lecture 2: Counting the Number of Combinations

Lecture 3: Generating Random Numbers

Lecture 4: Generating Random Sequences

Lecture 5: Converting Probabilities to Quantiles

Lecture 6: Criteria for Plotting a Density Function

Lecture 7: Concept of Data Manipulation

Lecture 8: Module on Machine Learning

Lecture 9: Machine Learning Concepts with R

Lecture 10: Machine Learning Datasets

Lecture 11: Machine learning project with R