Course Overview (2025 Edition)

(Last updated: Jul 30, 2025)

• Course Name: Data Science (2025/2026)
• Program: The second year of 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: 2024 version, 2023 version

All the content in this repository is licensed under CC BY 4.0.

Schedule Outline

We did this course for two years for the third-year Bachelor students, so the content is fairly stable. However, some parts may still need improvement, and this is the first time that we teach the course for second-year Bachelor students.

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:

• notebook: a Jupyter Notebook script
• tutorial: step-by-step guidences of a notebook script
• practice: a practice (ungraded) for self-study
• assignment: an asssignment (graded)

There is a preparation section for each lecture. We strongly recommend you to prepare them before going to the lectures or tutorials. If you come to the class without doing the preparation part, you may not be able to understand the contents of lectures or tutorials.

• Week 1
  • Lecture 1 (Sep 2 Tuesday): Course introduction, as well as demonstrations of data science’s social impact
  • Lecture 2 (Sep 4 Thursday): Fundamentals of data science techniques (e.g., table operations, classification, regression)
  • Practice 1: Python programming warm-up with Pandas and Numpy
  • Seminar (Sep 5 Friday): Work on Practice 1
• Week 2
  • Lecture 3 (Sep 9 Tuesday): Decision Tree and Random Forest for structured data processing
  • Lecture 4 (Sep 11 Thursday): Tutorial of the structured data processing module (using Jupyter Notebook)
  • Assignment 1: Structured data processing module
    • You need to submit the reflective writing by Sep 16 Tuesday at 23:59.
  • Seminar (Sep 12 Friday): Work on Assignment 1.
• Week 3
  • Lecture 5 (Sep 16 Tuesday): Overview of deep learning techniques and applications
  • Lecture 6 (Sep 18 Thursday): 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 (Sep 19 Friday): Work on the mock exams, Practice 2, and preparation for the mid-term exam.
• Week 4
  • Mid-term exam (Sep 23 Tuesday)
  • A lecture to discuss the mid-term exam questions (Sep 25 Thursday).

The mid-term exam covers materials from Lecture 1, Lecture 2, Lecture 3, Lecture 4, Lecture 5, Practice 1, and Assignment 1. Lecture 6 and Practice 2 are not covered in the mid-term exam.

• Week 5
  • Lecture 7 (Sep 30 Tuesday): Explain details in the pipeline of processing text data
  • Lecture 8 (Oct 2 Thursday): Tutorial for the text data processing module (using Jupyter Notebook)
  • Assignment 2: Text data processing module
    • You need to submit the reflective writing by Oct 7 Tuesday at 23:59.
  • Seminar (Oct 3 Friday): Work on Assignment 2.
• Week 6
  • Lecture 9 (Oct 7 Tuesday): Explain details in the pipeline of processing image data
  • Lecture 10 (Oct 9 Thursday): Tutorial for the image data processing module (using Jupyter Notebook)
  • Assignment 3: Image data processing module
    • You need to submit the reflective writing by Oct 14 Tuesday at 23:59.
  • Seminar (Oct 10 Friday): Work on Assignment 3.
• Week 7
  • Lecture 11 (Oct 14 Tuesday): Introduction of multimodal data processing
  • Lecture 12 (Oct 16 Thursday): Review of final exam materials (the first hour) and a guest lecture (the second hour).
  • Seminar (Oct 17 Friday): Work on the mock exams and prepare for the final exam.
• Week 8
  • Final exam (Oct 21 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. Lecture 12 is not covered in the final exam or resit.

• After this course
  • Q&A hour for the resit (check DataNose for the time)
  • Resit (check DataNose for the time)