| Week | Date | Topic | Lecture | Lab |
|---|---|---|---|---|
| 1 | Tue, Sep 23 | Introduction to DS & Programming |
π Overview
π Overview: Slides π Intro to Python |
π Setup
π Lab 1 |
| 2 | Tue, Sep 30 | Numerical Computation | π Intro to Numpy | π Lab 2 |
| 3 | Tue, Oct 7 | Data Wrangling | π Intro to Pandas | π Lab 3 |
| Thu, Oct 9 | Project proposal deadline (google form)β | |||
| 4 | Tue, Oct 14 | Visualization | π Intro to Plotting | π Lab 4 |
| 5 | Tue, Oct 21 | Data Science Workflow & Case Study | π Data Science Pipeline |
π Lab 5
π Lab 6 |
| 6 | Tue, Oct 28 | Guest Lectures, Catch up & Coaching | π¬ 1-1 group meetings | |
| Wed, Nov 12 | Project report deadline (moodle)β | |||
| Fri, Nov 14 | Project presentation deadline (moodle)β | |||
| 7 | Tue, Nov 18 | Final presentations (on site)β |
Introduction to Data Science I
MScAS 2025-2026
π§π»βπ« Teaching staff: Ilia Azizi (Lecturer).
π Time: Tuesdays 10:15-14:00, starting on the 23rd of September 2025.
π« Room: Internef 122 (no live broadcasting or recording).
π Schedule
π Content
The aim of this course is to learn the most important tools to work with and process data in Python, including concepts from statistics and computer science. Data preparation, data cleaning and data visualization are important aspects in managing acturial use cases (e.g. risk management, pricing, etc.). This course will set the foundations for learning about the treatment of data (machine learning is covered in Intro to DS II) and provide students with practical skills to:
- Apply fundamental principles of data science to actuarial and insurance problems
- Manipulate and analyze data using Python and its core libraries
- Implement statistical analysis and visualization techniques for actuarial datasets
- Design and execute complete data science pipelines from data collection to interpretation
- Communicate analytical results effectively through reports and presentations
The lectures will cover a wide range topics, including:
- Introduction to Python: Programming fundamentals and interfaces (scripting, jupyter, etc.)
- Introduction to NumPy: Numerical computing and array operations for actuarial calculations
- Data Manipulation with Pandas: Data cleaning, transformation and analysis workflows
- Visualisation with Matplotlib & Seaborn: Visualizations & exploratory data analysis (EDA)
- Data Science Pipeline: From data acquisition to interpretation with concrete examples
Note
The class will be hands-on and centered around data: bring your laptop to lectures!
π Evaluation
Applied project: individual or in group (depending on the number of participants to the course) with the following deliverables:
- One project proposal (submitted through a google form)
- One report (incl. supplementary material, codes, etc.)
- One final presentation will be organized during the semester.
ImportantImportant dates (more info during the class)
- Thursday the 9th October: Project proposal deadline
- Wednesday the 12th November: Project report deadline
- Friday the 14th of November: Slides submission deadline
- Tuesday the 18th of November: Presentations of the projects
Final grade = (0.2 x proposal) + (0.4 x report) + (0.4 x presentation)
CautionProject member contributions
The project grade is a group grade. However, if the contribution of the members of the groups to the project is unbalanced, an individual adaptation of the grade will be made, e.g., absence of one of the members of the group during the presentation (in catch-up, a subsequent and adapted presentation of the absent member may be required).
Further directives and guidelines are provided in the Assessment section.
π‘ Acknowledgment
This website, which serves as an original standalone book, was developed by Ilia Azizi with contributions from Aisan Azizi, building on top of actuarial content proposed by Sascha GΓΌnther and JosΓ© Miguel Flores-ContrΓ³. We thank them for their contributions.
There are many books that inspired this course. Here are some of them:
- Programming in Python:
- Matthes, E. (2023). Python crash course: A hands-on, project-based introduction to programming. 3rd ed. No Starch Press.
- Lutz, M. (2025). Learning Python: Powerful Object-Oriented Programming. 6th ed. OβReilly Media, Inc.
- Data science and analysis in Python:
- McKinney, W. (2022). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython, 3rd ed. OβReilly Media, Inc
- VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. OβReilly Media, Inc.
- Grus, J. (2019). Data Science from Scratch: First Principles with Python. OβReilly Media, Inc.
- Other relevant books (data science and acturial computations in R):
- Grolemund, G., & Wickham, H. (2023). R for Data Science, 2nd ed. OβReilly Media.
- McQuire, P., & Kume, A. (2023). R Programming for Actuarial Science. John Wiley & Sons.
- Charpentier, A. (Ed.). (2014). Computational actuarial science with R. CRC press.
π Citation
If you use this course material in your research or teaching, please cite it as:
Azizi, I. (2025). DSAS: Data Science for Actuarial Sciences in Python. https://unco3892.github.io/dsas/@misc{azizi2025dsas,
author = {Azizi, Ilia},
title = {DSAS: Data Science for Actuarial Sciences in Python},
year = {2025},
url = {https://unco3892.github.io/dsas/},
}