Introduction to Data Science and Analytics using R

Learn to create & test Machine Learning & Data Science Models in R from Data Science experts. Code templates included.

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!

What you’ll learn

  • Basics of statistical modelling.
  • Basics of data science using R and Python.
  • Forecasting and prediction using Data.
  • Data Visualisation.

Course Content

  • Installing R and R Studios –> 1 lecture • 5min.
  • Course Introduction –> 1 lecture • 5min.
  • Getting Started –> 1 lecture • 13min.
  • Introduction –> 2 lectures • 24min.
  • Missing value treatment and Measures of Dispersion –> 3 lectures • 54min.
  • Introduction to linear regression models –> 4 lectures • 1hr 27min.
  • Introduction to classification models –> 2 lectures • 43min.
  • Random Forest Models in R –> 3 lectures • 1hr 4min.

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  • No programming experience needed.

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
  • Multivariate Linear Regression using R (Multiple Linear Regression, R-square, Adjusted R-square, p-value, backward selection)

Part 5.) Building Classification Models in R

  • Classification using Logistic Regression
  • Logistic Regression and Generalized Linear Models in R & Measures of Accuracy for a Classification Models (AIC, AUC, Confusion Matrix, Precision, and Recall)

Part 6.) Random Forest Models in R

  • Introduction to decision tree classifier (trees package, Gini index, and tree pruning )
  • Creating decision tree and Random Forest in R (Random forest package in R, hyper-parameters tuning, visualizing a tree in R)
  • Building Random Forest Regressors


The course takes you through practical exercises that are based on real-life datasets to help you build models hands-on.

And as additional material, this course includes R code templates which you can download and re-use on your own projects.

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