DATA ANALYSIS FOR BUSINESS
Instructional goals
Provide students with practical skills to work with and summarize datasets, aiming to provide business insights.
Intended learning outcomes
The students will have a great ability to use statistics to provide business insights. Also, the continuous assessment program will enhance the student's skills in Python.
Course Contents
Python brief recap;
Principal Component Analysis;
Panel data regression;
Univariate time series models;
Basic of Machine Learning.
Reference Books
[SW] Stock, J.H. and Watson, M.V. (2020), Introduction to Econometrics, 4th edition (or earlier), Pearson
[ISL] James, G., Witten, D., Hastie, T., Tibshirani, R., and Taylor, J. (2023), An Introduction to Statistical Learning with Python, Springer
[HA] Hyndman, R.J., Athanasopoulos, G., Garza, A., Challu, C., Mergenthaler, M. and Olivares, K.G. (2026), Forecasting: Principles and Practice, the Pythonic way, freely available online at: https://otexts.org/fpppy/
Course material
Teaching Methods
Theory + Practical classes.
Assessment Method
Midterm test (30%, written) +
Group project (40%, oral presentation) +
Final exam (30%, written)
Thesis assignment criteria
TBD
Week 1
Python recap;
Principal Component Analysis
ISL chapter 12.2
HA appendix
Week 2
Principal Component Analysis
ISL chapter 12.2
Week 3
Panel regression
SW chapter 10.1-10.2
Week 4
Panel regression
SW chapter 10.3
Week 5
Panel regression
SW chapter 10.4
Week 6
Panel regression
SW chapter 10.5
Week 7
Time series models
SW chapter 15.1-2
HA chapter 9
Midterm test
Week 8
Time series models
SW chapter 15.3
HA chapter 9
Week 9
Time series models
SW chapter 15.6-7
HA chapter 9
Week 10
Time series models
HA chapter 9
Week 11
Intro to Machine Learning
Week 12
Intro to Machine Learning;
Course Recap
Project presentations