- Module 1: Introduction to Data Analytics
- 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.
- 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
- 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.
- Variables in R
- Data frames
- Using c, Cbind, Rbind, attach and detach functions in R
- 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.
- Data sorting
- Find and remove duplicates record
- Cleaning data
- Recoding data
- Merging data
- Slicing of Data
- Merging Data
- Apply functions
- 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.
- Reading Data
- Writing Data
- Basic SQL queries in R
- Web Scraping
- 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.
- Box plot
- Pareto charts
- Pie graph
- Line chart
- Developing graphs
- This module touches the base Statistics, Machine learning techniques used in the Industry and will cover case studies.
- Standard deviation
- Linear regression
- Multiple regression
- Logistic regressions
- 1 Real-time project
Module 2: Introduction to R programming
Module 3: Data Manipulation in R
Module 4: Data Import techniques in R
Module 5: Exploratory Data Analysis
Module 6: Overview of Machine Learning techniques
Module 7: Project Work