Course syllabus


DAT565 Introduction to Data Science & AI, LP1 HT23 (7.5 hp)

Course is offered by the department of Computer Science and Engineering

Course organisation

The course is divided into three parts:

  • Part I: Introduction to data science (3 weeks)
  • Part II: Statistical methods in data science and AI (2 weeks)
  • Part III: Introduction to AI (3 weeks)

Each part is in turn divided into weekly modules, with weekly assignments. The assignments are performed in small groups of 2-3 students, and the deadline for each assignment is Monday at 13:00 the following week.

Week Topic
1 Introduction to Data Science & Getting Started with Python
2 Regression and Classification
3 Clustering
4 Bayesian Statistics and Graphical Models
5 Markov Models, Kernel Methods and Decision Trees
6 Introduction to AI and its Ethics
7 Machine Learning and Neural Networks
8 Rule-based AI


Examiner: Rocío Mercado (


  • Rocío Mercado (week 1, 2, 3, 8)
  • Bastiaan Bruinsma (weeks 6, 7)
  • Milad Malekipirbazari (week 4, 5)

Teaching assistants:

  • Bastiaan Bruinsma
  • Milad Malekipirbazari
  • Adam Breitholtz
  • Firooz Shahriari Mehr 
  • Markus Pettersson
  • Mehrdad Farahani
  • Ying Qu
  • Venkata Sai Dinesh Uddagiri
  • Aditya Padmanabhan Varma
  • Johan Östling
  • Patrik Dennis
  • Mirco Ghadri

Contact Information

For questions about assignments you may either ask via the Discussion Forum in Canvas (as many other students likely have the same question) or ask TAs during a lab session (see instructions below). PMs or emails directly to teachers are discouraged. As there are many people involved teaching various parts of the course, the fastest and most reliable way to get a response is via Discussion Forum posts; this way everyone can see it and reply.

Course literature

The course has no mandatory textbooks, but the following books are recommended for consultation. The page for each module contains links to lecture slides and other supplementary material, including some links to video material.

Data Science

  • S.S. Skiena, The Data Science Design Manual, Springer, 2017. The e-book is available for free from the Chalmers library from within the Chalmers network.


Statistical Methods for Data Science and AI

Changes made since the last occasion

No substantial changes in content.

The course is now offered in hybrid format (lectures) with in-person lab sessions.

Grading for this course instance is pass/fail.

Learning objectives

On successful completion of the course the student will be able to:
Knowledge and understanding:
  • describe fundamental types of problems and main approaches in data science and AI,
  • give examples of data science and AI applications from different contexts ,
  • give examples of how stochastic models and machine learning (ML) are applied in data science and AI,
  • explain basic concepts in classical AI, and the relationship between logical and data driven, ML-based approaches within AI,
  • and briefly explain the historical development of AI, what is possible today, and discuss possible future development.
Skills and abilities:
  • use appropriate programming libraries and techniques to implement basic transformations, visualizations and analyses of example data,
  • identify appropriate types of analysis problems for some concrete data science applications ,
  • implement some types of stochastic models and apply them in data science and AI applications,
  • implement and/or use AI-tools for search, planning, and problem solving,
  • and apply simple machine learning methods implemented in a standard library.
Judgement and approach:
  • justify which type of statistical method is applicable for the most common types of experiments in data science applications, 
  • discuss advantages and drawbacks of different types of approaches and models within data science and AI,
  • reflect on inherent limitations of data science methods and how the misuse of statistical techniques can lead to dubious conclusions,
  • critically analyze and discuss data science and AI applications with respect to ethics, privacy and societal impact,
  • and show a reflective attitude in all learning.



Link to the syllabus on Studieportalen.

Study plan

Examination form

The examination is through weekly assignments, carried out in small groups of 2-3 studentsAlways submit only your own work, no copying of text or code is allowed. We will use automated plagiarism checkers.

The deadline for each week's assignment can be found at the foot of this page. You will have 1 week from the first lecture on the topic until the deadline. After the submission deadline, TAs will mark the assignment within 10 days, i.e., by the Thursday the following week.


The grading scales for this course instance is PASS/FAIL. Please read the text below carefully to understand the requirements that applies to you.

Weekly assignment grades:

  • Weekly assignments are only available for a specific time (see dates for each assignment). Should the assignment require revisions, these must be done during the time the assignment is available.
  • Weekly assignments are graded as PASS or FAIL. To pass, at least a first attempt must have been submitted before the first deadline. Should the assignment require revisions, these must be done during the time the assignment is available. At most one resubmission is permitted per assignment, and this must be submitted at most one week (7 days) from the date you receive your grade.
  • Extensions may be allowed by prior arrangements with the examiner for valid reasons (e.g., sickness, family issues). You must then contact the examiner as soon as possible to make such an agreement. Extensions will not be granted for, e.g., vacation trips.
  • You must pass all assignments to receive a passing grade in the class.

Course grades:

  • To pass the course, students must pass all weekly assignments. If so, the student will be given the grade PASS.

Course summary:

Date Details Due