Course Objective :
To equip students with founding concepts and techniques of statistics for building competence in business analytics and inference using R programming
Learning Outcomes :
- To be able to describe data sets and their characteristics.
- Select appropriate statistical tests for making managerial decisions
- Perform statistical inference
- Build predictive models to forecast the future trends
Course Duration: Minimum 65 - 75 hrs
Basics of Statistics [Module I]
- Exploratory Data Analysis
- Probability and Distributions
- Sampling Techniques
- Hypothesis Testing
- Chi Square Test
- ANOVA and Experimental Design
- Measures of Location and Spread
- Skewness, Kurtosis
- Chebyshev Theorem
- Brief Introduction to Probability & Distributions
- Sampling Techniques
Data Science Programming using R [Module II]
- Getting Started in R
- Basic building blocks in R
- Advance Data Structures in R
- Reading Data in R
- Basics of Programming
- Data Munging
- Manipulating Strings
- Basics Statistics in R
- Visualizations in R
- Linear models
- Machine Learning algorithms
- Web Scraping
- Text mining
Application of Data Science in the Industry [Module III]
(Any 2 of the below could be chosen based on Student interest)
- Retail Industry
- Marketing Industry
- Banking Sector
Evaluation
