Data Science for Business

2025/2026

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Finance

Course type:

Elective course

When:

1 year, 2 module

Instructor

Lambert, Jerome

Course Syllabus

Abstract

This course introduces the foundations of Python and machine learning to prepare students for advanced study. Through a mix of lectures and practical seminars, students learn to write clean Python code, work with data (loading, cleaning, visualizing), and understand core ML concepts such as supervised vs. unsupervised learning, model training/validation, and evaluation metrics. Using real datasets and standard libraries (e.g., pandas, scikit-learn, matplotlib), students build simple predictive models, interpret results, and communicate insights responsibly. By the end, learners can structure a small end-to-end workflow, from problem framing and data preprocessing to baseline modeling and performance assessment, equipping them with the skills and confidence needed for the follow-on course.

Learning Objectives

• Use Python for data work: Set up a reproducible workflow in Jupyter; load, clean, transform, and visualize finance datasets with pandas/NumPy/matplotlib
• Build baseline models: Train simple supervised models in scikit-learn (e.g., linear/logistic regression, trees, k-NN) with proper splits and pipelines
• Evaluate and reason about results: Apply suitable metrics (MAE/MAPE, accuracy/precision/recall/ROC-AUC), detect overfitting, and explain bias–variance trade-offs
• Communicate and collaborate: Present findings clearly with plots and short summaries, document code, and plan a small, ethical, finance-focused mini-project

Expected Learning Outcomes

• Source financial data: Identify and retrieve clean datasets from reputable providers (e.g., FRED, WRDS, Yahoo Finance/APIs) and document provenance
• Explain market structure: Describe and compare core features of money vs. capital markets, including instruments, participants, and typical risks
• Construct and assess portfolios: Build mean–variance portfolios, estimate inputs (returns, variance–covariance), and evaluate trade-offs using efficient frontier metrics

Course Contents

• Python Foundations for Data Science
• Data Acquisition & Wrangling
• Stats Essentials for ML
• ML Fundamentals for Finance

Assessment Elements

• Midterm test
• Final Examination

Interim Assessment

• 2025/2026 2nd module
  0.65 * Final Examination + 0.35 * Midterm test

Bibliography

Recommended Core Bibliography

• Aurélien Géron. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems: Vol. Second edition. O’Reilly Media.
• Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani. (2013). An Introduction to Statistical Learning : With Applications in R. Springer.

Recommended Additional Bibliography

• Müller, A. C., & Guido, S. (2017). Introduction to Machine Learning with Python : A Guide for Data Scientists: Vol. First edition. Reilly - O’Reilly Media.

Authors

• SOLOVEVA EKATERINA EVGENEVNA