Course

Discipline Specific Electives: Business Analytics

Unit 1

Introduction to Data Science – Basic concepts – Data – Nature – Process for Data Science – Handling Data. R software – core and optional packages – Data science packages – Exploratory Analytics using R –Visualizing Data– Applications

Unit 2

Overview of R programming, Environment setup with R Studio, R Commands, Variables and Data Types, Control Structures, Array, Matrix, Vectors, Factors, Functions, R packages.

Unit 3

Conditional statements such as If-then-else – Loop structures such as For, While, Repeat. Function – pre-defined functions, user-defined functions.

Unit 4

R for Basic Math, Arithmetic, Variables, Functions, Vectors, Expressions and assignments Logical expressions. Defining a Matrix, Sub-setting, Matrix Operations, Conditions and Looping: if statements, looping with for, looping with while, vector based programming.

Unit 5

Read & write data from and to various files and in various formats such as Text, Excel, CSV. Write programs to perform operations to inspect, profile, clean, and transform data from files, Using basic packages for advanced data operations

Objective:

Exposure to data science using R. Course Outcomes:

CO1: Knowledge on R programming.

CO2: To understand the basic commands in R.

CO3: Ability to apply statistical techniques using R Programming for data analytics and decision making

CO4: Knowledge of prescriptive analytics.

TEXTBOOK:

• An Introduction to R, Notes on R: A Programming Environment for Data Analysis and W. N. Venables, D.M. Smith and the R Development Core Team. Version 3.0.1.
• Jones, , Maillardet. R. and Robinson, A. (2014). Introduction to Scientific Programming and Simulation Using R. Chapman & Hall/CRC, The R Series.