Statistical Modelling with R and Python
Why colleges run this
Sound statistics separate real analysis from guesswork; students who can test hypotheses and model relationships bring rigour that employers across sectors need.
Course outcomes
CO1
Summarise a dataset using descriptive statistics and visualisation
K2CO2
Construct confidence intervals from a sample
K3CO3
Test a hypothesis and interpret the result
K4CO4
Fit and diagnose a regression model
K4CO5
Produce a statistical report on a real dataset
K5Modules tap a module for its theory & lab
Hours shown are the recommended 3-day format — module time scales to the duration you pick.
01Descriptive statistics4 h
Theory
Distributions, central tendency, spread, visualisation
Lab
Summarise a dataset with statistics and plots
Output
Descriptive report
02Probability and inference4 h
Theory
Sampling, confidence intervals
Lab
Construct confidence intervals from samples
Output
Inference notebook
03Hypothesis testing4 h
Theory
t-tests, chi-square, interpreting p-values
Lab
Run and interpret hypothesis tests
Output
Testing notebook
04Correlation and regression4 h
Theory
Linear regression, assumptions, diagnostics
Lab
Fit and diagnose a regression
Output
Regression notebook
05Multiple regression4 h
Theory
Multiple predictors, model selection
Lab
Build a multiple-regression model
Output
Multiple-regression notebook
06Reporting capstone4 h
Theory
A full statistical report on a real dataset
Lab
Produce a statistical report
Output
Statistical report
Every participant receives
Certificate of completion Course material LMS access Interview question bank Mock interview & viva practice Optional nasscom NSQF assessment