Introduction to Data Science and Analytics using R

Course description
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 RSetting up RGetting Started with R Studios IDE SwirlPart 2.) Introduction to Statistical MeasuresMeasures of Central TendenciesIntroduction to Data Science using RPart 3.) Data Processing and Data Visualisation in RMeasures of Dispersions and Outlier TreatmentMissing Value Treatment using RData Visualization using R ( boxplots, bubble plots, heat plots, automated-EDA in R)Part 4.) Building Regression Models in RLinear Regression TheoryLinear Regression using RMultivariate Linear Regression TheoryMultivariate Linear Regression using R (Multiple Linear Regression, R-square, Adjusted R-square, p-value, backward selection)Part 5.) Building Classification Models in RClassification using Logistic RegressionLogistic 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 RIntroduction 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 RegressorsThe 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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