Module 1- Introduction to Data Analytics

Objectives:

• This module introduces you to some of the important keywords in R like Business Intelligence, Business Analytics, Data and Information.
• You can also learn how R can play an important role in solving complex analytical problems.
• This module tells you what is R and how it is used by the giants like Google, Facebook, etc.
• Also, you will learn use of ‘R’ in the industry, this module also helps you compare R with other software in analytics, install R and its packages.

Topics

• Business Analytics, Data, Information
• Understanding Business Analytics and R
• Compare R with other software in analytics
• Install R
• Perform basic operations in R using command line
• Learn the use of IDE R Studio
• Use the ‘R help’ feature in R

Module 2- Introduction to R programming

Objectives:

• This module starts from the basics of R programming like datatypes and functions.
• In this module, we present a scenario and let you think about the options to resolve it, such as which datatype should one to store the variable or which R function that can help you in this scenario.
• You will also learn how to apply the ‘join’ function in SQL.

Topics

• Variables in R
• Scalars
• Vectors
• Matrices
• List
• Data frames
• Using c, Cbind, Rbind, attach and detach functions in R
• Factors

Module 3- Data Manipulation in R

Objectives:

• In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis.
• Thus using and exploring the popular functions required to clean data in R.

Topics

• Data sorting
• Find and remove duplicates record
• Cleaning data
• Recoding data
• Merging data
• Slicing of Data
• Merging Data
• Apply functions

Module 4- Data Import techniques in R

Objectives:

• This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a csv file to the data scraped from a website.
• This module teaches you various data importing techniques in R.

Topics

• Reading Data
• Writing Data
• Basic SQL queries in R
• Web Scraping

Module 5- Exploratory data Analysis

Objectives:

• In this module, you will learn that exploratory data analysis is an important step in the analysis.
• EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.

Topics

• Box plot
• Histogram
• Pareto charts
• Pie graph
• Line chart
• Scatterplot
• Developing Graphs

Module 6- Basics of Statistics & Linear & Logistic Regression

Objectives:

• This module touches the base of Descriptive and Inferential Statistics and Probabilities & ‘Regression Techniques’.
• Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.

Topics

• Basics of Statistics
• Inferencial statistics
• Probability
• Hypothesis
• Standard deviation
• Outliers
• Correlation
• Linear & Logistic Regression

Module 7- Data Mining: Clustering techniques, Regression & Classification

Objectives:

• Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.
• The two Machine Learning types are Supervised Learning and Unsupervised Learning and the difference between the two types.
• We will also discuss the process involved in ‘K-means Clustering’, the various statistical measures you need to know to implement it in this module.

Topics

• Introduction to Data Mining
• Understanding Machine Learning
• Supervised and Unsupervised Machine Learning Algorithms
• K- means clustering

Module 8- Anova & Sentiment Analysis

Objectives:

• This module tells you about the Analysis of Variance (Anova) Technique.
• The algorithm and various aspects of Anova have been discussed in this module
• Additionally, this module also deals with Sentiment Analysis and how we can fetch, extract and mine live data from Twitter to find out the sentiment of the tweets.

Topics

• Anova
• Sentiment Analysis

Module 9- Data Mining:  Decision Trees and Random Forest

Objectives:

• This module covers the concepts of Decision Trees and Random Forest.
• The algorithm of Random Forests is discussed in a step-wise approach and explained with real-life examples.

Topics

• Decision Tree
• Concepts of Random Forest
• Working of Random Forest
• Features of Random Forest

Module 10- Project work

• 2 Real-time projects