Bocconi University · MSc · Course 20598

Finance with Big Data

Financial theory meets modern data analysis: portfolio theory, asset pricing, and machine learning applied to real financial data — in Python, hands-on, every week.

Course Director & Instructor Clément Mazet-Sonilhac clement.mazetsonilhac@unibocconi.it
Teaching Assistant Andrea Andolfatto andrea.andolfatto@phd.unibocconi.it

How the course works

This site is the single reference for the course: everything you need — lecture slides, weekly assignments, datasets, and (after each deadline) full model solutions — lives here.

The course runs on a weekly rhythm:

  • Lectures cover the theory: from finance fundamentals to portfolio theory, asset pricing, and machine learning methods applied to financial data.
  • PC Labs (PC Labs) are weekly hands-on projects, done in groups, in Python. Each lab is written as a short case study: you are dropped into a realistic professional situation and asked to solve a concrete problem with real data. You submit a Jupyter notebook; after the deadline, a complete model solution is published on the lab page.

Check the schedule below for what’s due and when. New material appears here every week. For grading, group rules, software setup, and everything practical, see Course Info.

What you'll build

Every PC Lab ends with something concrete: a working notebook, real data, and charts like these.

Scatter of simulated portfolios by risk and return, colored by Sharpe ratio, with the tangency portfolio marked
Build the efficient frontier and find the tangency portfolio — PC Lab 1
Daily Twitter sentiment index over one year of trading days
Turn thousands of tweets into a sentiment factor — PC Lab 3
K-means clusters of bank customers by credit limit and spending score
Segment bank customers with K-means — PC Lab 7

Schedule

WeekLecturesPC LabLab due
Week 1 Introduction & Finance 1-0-1 — —
Week 2 Portfolio Theory PC Lab 1 — Applied Portfolio Theory Fri, midnight (Week 2)
Week 3 CAPM and APT PC Lab 2 — Applying the CAPM Fri, midnight (Week 3)
Week 4 Empirical Asset Pricing PC Lab 3 — Creating a Factor from Text Data Fri, midnight (Week 4)
Week 5 Asset Pricing with Big Data PC Lab 4 — Predicting Stock Returns with ML Fri, midnight (Week 5)
Week 6 — Hackathon — NGO Campaigns & Responsible Investment 2 weeks — Fri, midnight (Week 7)
Week 7 Microeconomics of Banking PC Lab 6 — Predicting Credit Scores with ML Fri, midnight (Week 7)
Week 8 Banking in the Age of AI PC Lab 7 — Bank Customers Segmentation Fri, midnight (Week 8)
Week 9 Financial Intermediation in the Age of AI PC Lab 8 — Measuring Startup Innovation Optional — not graded
Week 10 Payment Technology & Foundations of Blockchain — —
Week 11 Cryptocurrency and Smart Contracts — —

The schedule fills in as the semester progresses — new lectures and labs appear here every week.

Ask, answer, argue

A stuck cell, a result you cannot interpret, a paper worth arguing about — the course forum is open to the whole class. The teaching team answers there, and so can your classmates.

Open the course forum →