Introduction to Data Science and Analytics using R

Are you interested in the field of Data Science and Machine Learning but haven’t had experience in it? Then this course is for you!

This course has been designed by a professional Data Scientist so that I can share my knowledge and industry experience and help you learn the basics of data science algorithms and coding libraries.

This course includes a step–by–step approach to Data Science and Machine Learning. With each lecture, you will develop the mathematical understanding as well as the understanding of necessary libraries to help you ace Data Science interviews and enter into this field.

The course is structured in a very crisp and comprehensive manner to help you understand industry–relevant algorithms. It is structured the following way:

Part 1.) Getting started with R

Setting up R

Getting Started with R Studios IDE

Swirl

Part 2.) Introduction to Statistical Measures

Measures of Central Tendencies

Introduction to Data Science using R

Part 3.) Data Processing and Data Visualisation in R

Measures of Dispersions and Outlier Treatment

Missing Value Treatment using R

Data Visualization using R ( boxplots, bubble plots, heat plots, automated–EDA in R)

Part 4.) Building Regression Models in R

Linear Regression Theory

Linear Regression using R

Multivariate Linear Regression Theory