R Programming:
–>Intro to R Programming
Introduction to R
Business Analytics
Analytics concepts
The importance of R in analytics
R Language community and eco-system
Usage of R in industry
Installing R and other packages
Perform basic R operations using command line
Usage of IDE R Studio and various GUI
–>R Programming Concepts
The datatypes in R and its uses
Built-in functions in R
Subsetting methods
Summarize data using functions
Use of functions like head(), tail(), for inspecting data
Use-cases for problem solving using R
–>Data Manipulation in R
Various phases of Data Cleaning
Functions used in Inspection
Data Cleaning Techniques
Uses of functions involved
Use-cases for Data Cleaning using R
–>Data Import Techniques in R
Import data from spreadsheets and text files into R
Importing data from statistical formats
Packages installation for database import
Connecting to RDBMS from R using ODBC and basic SQL queries in R
Web Scraping
Other concepts on Data Import Techniques
–>Exploratory Data Analysis (EDA) using R
What is EDA?
Why do we need EDA?
Goals of EDA
Types of EDA
Implementing of EDA
Boxplots, cor() in R
EDA functions
Multiple packages in R for data analysis
Some fancy plots
Use-cases for EDA using R
–>Data Visualization in R
Storytelling with Data
Principle tenets
Elements of Data Visualization
Infographics vs Data Visualization
Data Visualization & Graphical functions in R
Plotting Graphs
Customizing Graphical Parameters to improvise the plots
Various GUIs
Spatial Analysis
Other Visualization concepts
