Course syllabus
Course-PM
DAT565 / DIT407 DAT565 / DIT407 Introduction to data science and AI lp4 VT25 (7.5 hp)
Course is offered by the department of Computer Science and Engineering
Contact details
| Lecturer: | Dr. Oana Geman | <geman@chalmers.se> |
| Lecturer/Examiner: | Dr. Moa Johansson | <moa.johansson@chalmers.se> |
| Lecturer: | Dr. Stefano Sarao Manelli | <s.saraomannelli@chalmers.se> |
Doctoral students TAs:
| TBA | |
Student TAs:
| TBA | |
Course Representatives:
Course purpose
The course gives a broad introduction to various techniques and theories used in Data Science and AI, with particular focus on their practical applications.
Schedule
The course includes 2 in-person lectures each week, as well as 1 laboratory session. The schedule and location for lectures and sessions varies each week due to holidays and other reasons beyond our control. For the latest information on time and locations for the week, please see TimeEdit.
For an overview of all lectures, assignments and responsible Teaching Assistents, please see: Lectures and Assignments (TBA)
Course literature
Skiena, Steven S. (2017). The Data Science Design Manual. Springer.
Available through Chalmers Network and Library at https://link.springer.com/book/10.1007/978-3-319-55444-0 (Link opens an external website)
Additional literature can be found in their respective modules.
Course design
The course has eight mandatory assignments, where the important dates are given in Canvas. It is the student's responsibility to ensure you know when assignments are released and when they are due.
Assignment deadlines are hard and no extensions are given. The only exception is for illness or similar, in which case you must contact the examiner and relevant head TA for that assignment via email immediately to seek approval. No extensions are given for holiday trips. You will receive feedback for the assignments one week after the deadline, based on the version that was submitted by the deadline.
Programming assignments are done in pairs. Quizzes are done individually.
In order to pass the course, you must pass all assignments. An assignment is considered a pass if you have obtained at least 70% of the maximum score.
Lab sessions consist of independent work on the assignments and there will be course staff available for help, but are not strictly mandatory. Lectures are to be considered mandatory, although no attendance will be taken.
There is reading attached for each lecture. You are supposed to read that before the lecture. The lectures are structured under the assumption that you have read the text in the course textbook, and the content of the textbook will not be repeated. Instead, the lectures complement the textbook and offer further examples, proofs, details etc.
Policy on generative AI
The use of text-generating tools for generating assignment reports is forbidden. You are supposed to write your reports yourself.
Furthermore, it is discouraged that you use tools such as ChatGPT for "searching for content". Generative AI is very prone to hallucinating and producing convincing, yet completely wrong answers. Do not rely on such tools. You cannot know whether the answer is correct or not, unless you already know the answer. We will discuss this problem towards the end of the course.
Plagiarism policy
You are not allowed to copy pieces of code from students of other groups. You may discuss the problems, but you may not share code.
You may not publish your solutions. Do not put your code into a public GitHub repository, for example.
If you use (small amounts of) materials you find in the Internet (e.g., Wikipedia, Stack Overflow, Reddit discussions), you must attribute the source. Finding matching code snippets without proper attribution means you are presenting others’ work as your own, and is considered plagiarism. As a guideline, less than 10% of your code should fall into this category. Why? Because you need to practice writing good code yourself, it's not guaranteed that anything from the internet is as good as one thinks...
Cases where plagiarism is spotted will be deferred to the disciplinary committee of the university and may lead to suspension.
Tools & Python libraries
The following Python libraries are going to be used on this course:
If you want to run these tools on your own computer, it is strongly recommended that you acquire an environment through Anaconda. Anaconda provides support for multiple environments and package management, and can be run on the most popular operating systems (Microsoft Windows, Apple MacOS, GNU/Linux). Anaconda installation also enables easy use of Jupyter Notebooks.
Changes made since the last occasion
To ensure also theoretical aspects of the syllabus are examined, some weekly assignments are replaced by individual quizzes to be taken in Canvas. More details will be provided when the course starts.
Learning objectives and syllabus
Learning objectives:
- 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.
- briefly explain the historical development of AI, what is possible today and discuss possible future development.
- 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
- apply simple machine learning methods implemented in a standard library
- 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
- show a reflective attitude in all learning
Link to the syllabus on Studieportalen.
https://www.chalmers.se/en/education/your-studies/find-course-and-programme-syllabi/course-syllabus/DAT565 / DIT407/?acYear=2025/2026
If the course is a joint course (Chalmers and Göteborgs Universitet) you should link to both syllabus (Chalmers and Göteborgs Universitet).
Examination form
The course is graded PASS/FAIL (G/U).
There is no particular exam. Instead, the course is considered PASS when one has passed all mandatory assignments. Assignments are done in groups of two students. Quizzes are taken individually, no collaboration is allowed.
Course summary:
| Date | Details | Due |
|---|---|---|