Data Science

Course manual 2025/2026

Course content

This course aims to familiarize you with various data science pipelines using examples with different data types. It is suitable for students who already have some experience in processing data and will work (or are currently working) with a large amount of data, especially focusing on obtaining insights from data through prediction or explanation techniques. This course is not intended to cover all topics in data science exhaustively. Instead, it introduces ways of working with structured (e.g., sensor measurements) and unstructured data (e.g., text and image) using machine learning and deep learning techniques. Additionally, it also introduces topics on multi-modal learning.

For more information, check https://multix.io/data-science-book-uva/syllabus.html

Lectures (hoorcollege) will be given in English, as well as all the teaching materials (e.g., lecture slides, Jupyter notebooks) and assessment materials (e.g., exam questions, exam instructions, assignment content). Seminars (werkcollege) will given in either Dutch or English, depending on the TA’s choice.

Study materials

Syllabus

• Check https://multix.io/data-science-book-uva/syllabus.html

Objectives

• Explain important components in the entire data science pipeline.
• Explain common data science modeling techniques and evaluation metrics.
• Use the Python Pandas and Numpy libraries to preprocess structured data.
• Implement deep learning modeling techniques using the Python PyTorch library.
• Perform given data science tasks and experiments with images, text, and structured data using Python.
• Reflect critically on the model performance using experiments with different settings and metrics.
• Obtain meaningful insights from data analysis for the given data science tasks.

Teaching methods

• Lecture
• Seminar
• Self-study

In the lectures, students learn the theory concepts (using slides) and how to apply the concepts (using Jupyter Notebooks). In the seminars, TAs give recitations of course topics and provide opportunities for students to ask questions.

For more information, check https://multix.io/data-science-book-uva/syllabus.html

Learning activities

Attendance

Additional requirements for this course:

No attendance requirement for this course.

Assessment

There is one resit for the course, which counts as 90% weight. There is no opportunity to do the resit for the reflective writing (10% weight). Your resit score will override the weighted sum of your mid-term and final exam grades.

For more information, check https://multix.io/data-science-book-uva/syllabus.html#grading

Inspection of assessed work

The manner of inspection will be communicated via the digitial learning environment.

Check https://multix.io/data-science-book-uva/syllabus.html

Assignments

Check https://multix.io/data-science-book-uva/syllabus.html

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Contact information

Coordinator

• Y. Hsu