### INTRODUCTION

As per the TIOBE Index for May 2020, R is among the top 10 programming languages.

Machine Learning Using R Studio: As per the TIOBE Index for May 2020, R is among the top 10 programming languages. It is widely used by data science professionals and statisticians because of its wide variety of statistical techniques, and visualization capabilities. R is an open source language, developed by Robert Gentleman and Ross Ihaka at the University of Auckland in the nineties. There are large number of functions (in-built) and thousands of packages for specific problems/scenarios. The packages are very well documented and find application in wide range of industries including research, academia, finance, marketing, genomics etc. The course is for the participants who have no programming background. The course is self contained and covers all the fundamentals of programming. The course covers R, Statistics and advanced machine learning techniques. This is a live instructor-led program where the participants can ask questions and clear their doubts instantly.

## UNIT 1: STATISTICS FOUNDATION • Measures of Central Tendency
• Measures of Variation-Dispersion, Skewness,Kurtosis
• Random Variables
• Probability , Bayes Theorem
• Probability distribution-Discrete and Continuous
• Types of Sampling Techniques
• Understanding Sample Distribution , Sampling Distribution
• Central Limit Theorem
• Confidence Interval Estimation, Z-Score, Confidence Level
• Hypothesis Testing
• Z-Test ,T-Test ,F-Test ,ANOVA
• Chi square Tests
• Types of Error
• Correlation

## 2. ANALYTICS FOR CRIME PREVENTION • R and R studio Installation and Overview
• Data structures– Vectors, Matrices, Factors, strings, List, Dataframe
• Relational, Logical and Arithmetic Operators, Precedence
• Indexing, Sub setting different data structures
• Creating User defined Functions In R
• Various control structures-For, while Loop etc
• Important inbuilt functions and their usage on data frame
• Data Visualization basics-histogram, bar-plot, pie-chart ,scatter plot etc
• Importing and reading data from the web

## UNIT 3: MACHINE LEARNING USING R STUDIO • Data wrangling, Exploratory Data analysis etc
• Understanding various Machine Learning Models and their application
• Evaluating and improving the model, Key Performance measures
• Installation and usage of Important libraries
• Simple and Multiple Linear Regression
• Logistic Regression
• Support vector machines
• Decision Trees-Classification and Regression Trees(CART)
• Random Forest
• K Nearest Neighbours
• Clustering – K means clustering
• Text Analytics-Sentiment Analytics
• Time Series
• Linear Discriminant analysis
• Principal Component Analysis
• Association Rules Mining
• Advanced Data visualization using Ggplot

## Machine Learning Using R Studio:     