Course Syllabus
Learning objectives, schedule, grading policy, and course requirements
📄 Note: View the Professor's Official Syllabus for the most up-to-date course details and schedule
Course Overview
Learning Objectives
Master Python, statistical learning, and high-performance computing for quantitative analysis in Economics and Finance.
Course Structure
Three 45-minute lectures + one 45-minute hands-on session per week with practical applications.
Skills You'll Master
Python Programming
Clean, efficient code • NumPy & Pandas • Visualization
Statistical Learning
Bias–variance • Model assessment • ML algorithms
Machine Learning
Regression • Classification • Tree methods • Neural networks
High-Performance Computing
Code acceleration • Parallel processing • Optimization
Project Management
End-to-end projects • Version control • Presentation
Course Schedule
Detailed weekly materials are available in the Weekly Materials Hub.
Python Foundations
Weeks 1-6Data Science
Weeks 7-10Advanced Topics
Weeks 11-14Assessment & Grading
Individual Project
Python programming project • 10-page report • GitHub repository • Optional presentation
View Project Guidelines →No Exams
Assessment based entirely on demonstrating understanding through the capstone project
Bonus Points
Additional opportunities through homework assignments throughout the semester
Course References & Textbooks
An Introduction to Statistical Learning
James, Witten, Hastie, Tibshirani (2nd Edition)
Probabilistic Machine Learning: An Introduction
Kevin P. Murphy • MIT Press
Introduction to Computation and Programming Using Python
John V. Guttag • MIT Press
Comprehensive introduction to Python programming
A Primer on Scientific Programming with Python
Hans Petter Langtangen • Springer
Focus on scientific computing applications