Course Overview (2026 Edition)
(Last updated: Jan 22, 2026)
- Course Name: Data Science (2025/2026)
- Program: Bachelor Informatiekunde (i.e., Information Science)
- Institution: Informatics Institute, University of Amsterdam
- Instructor: Yen-Chia Hsu <y.c.hsu@uva.nl>
- Refer to the course syllabus for details.
- Refer to DataNose for the time table and classroom location.
- Refer to Canvas for announcements, links to live lectures, and teaching team members’ emails.
- Previous editions: 2025 version, 2024 version, 2023 version
All the content in this repository is licensed under CC BY 4.0.
Schedule Outline
This section provides the outline of weekly activities. We strongly recommend you bring your laptop during the lectures for classroom activities.
Below are the explanation of terms:
tutorial: step-by-step guidences of a Jupyter Notebook scriptpractice: a practice (ungraded) for self-studyassignment: an asssignment (graded)
There is a preparation section for each lecture. You need to prepare them before going to the lectures or tutorials. If you come to the class without doing the preparation, you may not be able to understand the contents of lectures or tutorials well.
- Week 1
- Lecture 1 (Feb 3 Tuesday): Course introduction, as well as demonstrations of data science’s social impact
- Lecture 2 (Feb 4 Wednesday): Fundamentals of data science techniques (e.g., table operations, classification, regression)
- Practice 1: Python programming warm-up with Pandas and Numpy
- Seminar (Feb 5 Thursday): Work on Practice 1
- Week 2
- Lecture 3 (Feb 10 Tuesday): Decision Tree and Random Forest for structured data processing
- Lecture 4 (Feb 11 Wednesday): Tutorial of the structured data processing module (using Jupyter Notebook)
- Assignment 1: Structured data processing module
- Seminar (Feb 12 Thursday): Work on Assignment 1.
- Week 3
- Lecture 5 (Feb 17 Tuesday): Overview of deep learning techniques and applications
- Lecture 6 (Feb 18 Wednesday): Tutorial of the PyTorch deep learning framework (the first hour) and review of mid-term exam materials (the second hour)
- Practice 2: PyTorch implementation of structured data processing
- Seminar (Feb 19 Thursday): Work on the mock exams, Practice 2, and preparation for the mid-term exam.
- Week 4
- Mid-term exam (Feb 27 Friday)
The mid-term exam covers materials from Lecture 1, Lecture 2, Lecture 3, Lecture 4, Lecture 5, Lecture 6, Practice 1, Practice 2, and Assignment 1.
- Week 5
- An online lecture to discuss the mid-term exam questions (Mar 2 Monday).
- Lecture 7 (Mar 3 Tuesday): Explain details in the pipeline of processing text data
- Lecture 8 (Mar 4 Wednesday): Tutorial for the text data processing module (using Jupyter Notebook)
- Assignment 2: Text data processing module
- Seminar (Mar 5 Thursday): Work on Assignment 2.
- Week 6
- Lecture 9 (Mar 10 Tuesday): Explain details in the pipeline of processing image data
- Lecture 10 (Mar 11 Wednesday): Tutorial for the image data processing module (using Jupyter Notebook)
- Assignment 3: Image data processing module
- Seminar (Mar 12 Thursday): Work on Assignment 3.
- Week 7
- Lecture 11 (Mar 17 Tuesday): Introduction of multimodal data processing
- Lecture 12 (Mar 18 Wednesday): Guest lecture by TU Delft Assistant Professor Himanshu Verma (the first hour) and review of final exam materials (the second hour). Note that the guest lecture will NOT be recorded.
- Seminar (Mar 19 Thursday): Work on the mock exams and prepare for the final exam.
- Week 8
- Final exam (Mar 24 Tuesday)
The final exam and the resit covers materials from Lecture 1, Lecture 2, Lecture 3, Lecture 4, Lecture 5, Lecture 6, Lecture 7, Lecture 8, Lecture 9, Lecture 10, Lecture 11, Practice 1, Practice 2, Assignment 1, Assignment 2, and Assignment 3. Notice that Lecture 12 is not covered in the final exam or resit.
- After this course
- Q&A hour for the resit (May 20 Wednesday)
- Resit (May 27 Wednesday)