Data Science in Finance
Financial analytics, predictive modelling and decision intelligence.
What it covers
How financial data is sourced, cleaned, analysed and put to work — using Excel, SQL, Python and modern cloud platforms. You start with financial data basics and reporting, then progress to quantitative modelling, time series, alternative data and production ML engineering.
Why learn it now
Indian financial institutions have far more data than they can currently use, and regulatory reporting keeps getting heavier. Analytics has become the common language across risk, finance, product and compliance teams. People who can move from a spreadsheet to a model to a production pipeline are consistently short in supply.
Where it's applied
Portfolio Analysis & Backtesting Options Pricing & Greeks Risk Analytics Alternative Data Signals Market Sentiment Analytics Regulatory Reporting Automation Real-Time Market Data Pipelines Alternative Data Signals Cloud Data Platforms for Banking
Career pathways
Financial Data Analyst Quantitative Analyst Data Scientist — BFSI Data Engineer Risk Analytics Specialist Business Intelligence Analyst MLOps Engineer Investment Research Analyst Cloud Data Architect Head of Data & Analytics Financial Data Analyst Quantitative Analyst
Who should learn
Students, early-career professionals, CAs and finance professionals, banking and NBFC staff, MIS and reporting teams, analysts and investment professionals, engineers moving into finance, risk and compliance teams, and educators.
Courses
Courses are being added.