Course Overview (2025 Edition)

(Last updated: Jan 13, 2025)

• Course Name: Data Science (2024/2025)
• Program: The third 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, so the content is fairly stable. However, some parts may still need improvement.

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 (Feb 4 Tuesday): Course introduction, as well as demonstrations of data science’s social impact
  • Lecture 2 (Feb 5 Wednesday): Fundamentals of data science techniques (e.g., table operations, classification, regression)
  • Practice 1: Python programming warm-up with Pandas and Numpy
  • For the seminar, work on Practice 1.
• Week 2
  • Lecture 3 (Feb 11 Tuesday): Decision Tree and Random Forest for structured data processing
  • Lecture 4 (Feb 12 Wednesday): Tutorial of the structured data processing module (using Jupyter Notebook)
  • Assignment 1: Structured data processing module
    • You need to submit the reflective writing by Feb 18 Tuesday at 23:59.
  • For the seminar, work on Assignment 1.
• Week 3
  • Lecture 5 (Feb 18 Tuesday): Overview of deep learning techniques and applications
  • Lecture 6 (Feb 19 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
  • For the seminar, work on the mock exam, Practice 2, and preparation for the mid-term exam.
• Week 4
  • Mid-term exam (Feb 25 Tuesday)
  • A lecture to discuss the mid-term exam questions (Feb 26 Wednesday)

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 (Mar 4 Tuesday): Explain details in the pipeline of processing text data
  • Lecture 8 (Mar 5 Wednesday): Tutorial for the text data processing module (using Jupyter Notebook)
  • Assignment 2: Text data processing module
    • You need to submit the reflective writing by Mar 11 Tuesday at 23:59.
  • For the seminar, work on Assignment 2.
• Week 6
  • Lecture 9 (Mar 11 Tuesday): Explain details in the pipeline of processing image data
  • Lecture 10 (Mar 12 Wednesday): Tutorial for the image data processing module (using Jupyter Notebook)
  • Assignment 3: Image data processing module
    • You need to submit the reflective writing by Mar 18 Tuesday at 23:59.
  • For the seminar, work on Assignment 3.
• Week 7
  • Lecture 11 (Mar 18 Tuesday): Introduction of multimodal data processing
  • Lecture 12 (Mar 19 Wednesday): Review of final exam materials (the first hour) and a guest lecture (the second hour) by Dr. Ioannis Petros Samiotis, a postdoc researcher at CWI (Centrum Wiskunde & Informatica).
  • For the seminar, work on the mock exam and prepare for the final exam.
• Week 8
  • Final exam (Mar 25 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
  • Resit (check DataNose for the date)