Course Overview

(Last updated: Feb 9, 2024)

  • Course Name: Data Science (2023/2024)
  • Program: The third year of Bachelor Informatiekunde (i.e., Information Science)
  • Institution: Informatics Institute, University of Amsterdam
  • Instructor: Yen-Chia Hsu <>
  • 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: 2023 version

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

Schedule Outline

This course is still under development (the second iteration), and the schedule may be changed.

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 6): Course introduction, as well as demonstrations of data science’s social impact
    • Lecture 2 (Feb 8): 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 or do the preparation parts for lectures 2 and 3.
  • Week 2
    • Lecture 3 (Feb 13): Tutorial of the structured data processing module (using Jupyter Notebook)
    • Lecture 4 (Feb 15): Decision Tree and Random Forest for structured data processing
    • Assignment 1: Structured data processing module
    • For the seminar, work on Assignment 1.
  • Week 3
    • Lecture 5 (Feb 20): Overview of deep learning techniques and applications
    • Lecture 6 (Feb 22): 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
    • Check the mock exam page to prepare for next week’s mid-term exam.
    • For the seminar, work on the mock-up exam, Practice 2, and preparation for the mid-term exam.
  • Week 4
    • Mid-term exam (Feb 27)
    • A lecture to discuss the mid-term exam questions (Feb 29)

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
  • Week 6
  • Week 7
    • Lecture 11 (Mar 19): Introduction of multimodal data processing (the first hour) and review of final exam materials (the second hour)
    • Lecture 12 (Mar 21): Review of mock final exam (the first hour) and a remote guest lecture (the second hour) by Dr. Ting-Hao ‘Kenneth’ Huang about using crowdsourcing in data science research.
    • Check the mock exam page to prepare for next week’s final exam.
    • For the seminar, there is no assignment this week. Use the time to prepare for the final exam.
  • Week 8
    • Final exam (Mar 26)

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)