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 script
• practice: a practice (ungraded) for self-study
• assignment: 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: Course introduction, as well as demonstrations of data science’s social impact
  • Lecture 2: Fundamentals of data science techniques (e.g., table operations, classification, regression)
  • Practice 1: Python programming warm-up with Pandas and Numpy
  • Seminar 1: Work on Practice 1
• Week 2
  • Lecture 3: Decision Tree and Random Forest for structured data processing
  • Lecture 4: Tutorial of the structured data processing module (using Jupyter Notebook)
  • Assignment 1: Structured data processing module
  • Seminar 2: Work on Assignment 1.
• Week 3
  • Lecture 5: Overview of deep learning techniques and applications
  • Lecture 6: 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
  • Submit the reflective writing for Assignment 1.
  • Seminar 3: Work on the mock exams, Practice 2, and preparation for the mid-term exam.
• Week 4
  • Mid-term exam
  • An online lecture to discuss the mid-term exam questions.

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
  • Lecture 7: Explain details in the pipeline of processing text data
  • Lecture 8: Tutorial for the text data processing module (using Jupyter Notebook)
  • Assignment 2: Text data processing module
  • Seminar 4: 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
  • Submit the reflective writing for Assignment 2.
  • Seminar 5: Work on Assignment 3.
• Week 7
  • Lecture 11: Introduction of multimodal data processing
  • Lecture 12: Guest lecture (the first hour) and review of final exam materials (the second hour). Note that the guest lecture will NOT be recorded.
  • Seminar 6: Work on the mock exams and prepare for the final exam.
  • Submit the reflective writing for Assignment 3.
• Week 8
  • Final exam

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
  • Resit