Quantitative Analytics for Finance and Business
Why colleges run this
Forecasting and risk analytics drive decisions in every business; students who can build a working forecasting model bring directly employable quantitative skills.
Course outcomes
CO1
Describe the quantitative analytics workflow for a business problem
K2CO2
Decompose a time series into trend and seasonality
K4CO3
Build a forecasting model using appropriate methods
K3CO4
Evaluate forecast accuracy using error metrics
K4CO5
Produce a forecast with a documented business recommendation
K5Modules tap a module for its theory & lab
Hours shown are the recommended 3-day format — module time scales to the duration you pick.
01Business analytics foundations4 h
Theory
The quantitative workflow, data sources
Lab
Frame a business forecasting question
Output
Framing note
02Time series basics4 h
Theory
Trend, seasonality, decomposition
Lab
Decompose a business time series
Output
Decomposition notebook
03Forecasting methods4 h
Theory
Moving averages, exponential smoothing, ARIMA basics
Lab
Build competing forecasts
Output
Forecast notebook
04Risk fundamentals4 h
Theory
Variability, scenario analysis
Lab
Run a scenario analysis
Output
Scenario table
05Model evaluation4 h
Theory
Forecast-error metrics, backtesting
Lab
Backtest and score forecasts
Output
Evaluation report
06Forecasting capstone4 h
Theory
A working forecasting model on business data
Lab
Produce a forecast with a recommendation
Output
Forecast + recommendation
Every participant receives
Certificate of completion Course material LMS access Interview question bank Mock interview & viva practice Optional nasscom NSQF assessment