Course syllabus

Course-PM

DAT566 / DIT408 - Introduction to Data Science and AI - LP1 HT26 (7.5 hp)

Course is offered by the Department of Computer Science and Engineering.

Contact details

Examiner:  Kıvanç Tatar tatar@chalmers.se
Lecturer:    Oana Geman geman@chalmers.se
Lecturer:    Farzaneh Jalalypour farjal@chalmers.se
Lecturer:  Bastiaan Bruinsma sebastianus.bruinsma@chalmers.se
Teaching Assistant: Ziyuan Wang wangzi@chalmers.se
Teaching Assistant: Ioannis Savvas savvasi@chalmers.se
Teaching Assistant: Yossra Gharbi yossra@chalmers.se
Teaching Assistant: Ricardo Muñoz Sánchez ricardo.munoz.sanchez@svenska.gu.se
Teaching Assistant: Changheon Han  changheon.han@chalmers.se
Teaching Assistant: Herman Bergström hermanb@chalmers.se
Teaching Assistant: Emilie Francis emilie.francis@gu.se
Teaching Assistant: Mengyu Huang mengyuh@chalmers.se
Teaching Assistant: Yaochen Rao yaochenr@chalmers.se

Additional contact information is available on the Home page for student TAs and 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

Generally, the course includes 2 lectures and 2 laboratory sessions each week.

The schedule for lectures and sessions varies due to holidays. For the latest information, please see: TimeEdit

For an overview of all lectures, lab sessions, and responsible Teaching Assistants, please see: Front page

 

Assignment/quiz schedule

Please note that the deadline does NOT end at midnight. It is essential to plan accordingly.

Assignment/quiz #

Topic

Released

Deadline

Re-submission deadline

Module 1 Assignment

Weather

Thu 3 Sep 08:00

Friday 11 Sep 16:59

 Friday 25 Sep 16:59

Module 2 Assignment

Apartments

Thu 10 Sep 08:00

Friday 18 Sep 16:59

 Friday 2 Oct 16:59

Module 3 Assignment

Diabetes

Thu 17 Sep 08:00

Friday 25 Sep 16:59

 Friday 9 Oct 16:59

Modules 1-4 Quiz

Quiz 1

Thu 24 Sep 08:00

Friday 2 Oct 16:59

 Friday 16 Oct 16:59

Module 5 Assignment

Seeds

Thu 1 Oct 08:00

Friday 9 Oct 16:59

 Friday 23 Oct 16:59

Module 6 Assignment

Fashion MNIST

Thu 8 Oct 08:00

Friday 16 Oct 16:59

 Friday 30 Oct 16:59

Module 7 Assignment

RAG with Ollama

Thu 15 Oct 08:00

Friday 23 Oct 16:59

 Friday 30 October 16:59

Modules 5-8 Quiz

Quiz 2

Thu 22 Oct 08:00

Monday 26 Oct 16:59

 Friday 30 Oct 16:59

All times/dates are in Gothenburg, SE time. 

 

Lab sessions

Lab sessions will be held in only in person.

 

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

Additional literature can be found in the respective modules.

 

Course design

There is no mandatory attendance at either the Lectures or Laboratory sessions. Come if you see value in it and would like to interact with your lecturers and TAs, who put a lot of work into preparing interesting and engaging course material for you.

Laboratory sessions consist of independent work on the assignments and there will be course staff available to help. The teaching assistants will primarily answer questions related to the assignment of the week, and you are assumed to have attended the lectures and done the weekly readings. General questions about how to install Python (and similar) will only be answered if there is time. There are plenty of resources for this online, please see material for weeks 1-2 of the course.

Lectures are to be held in-person. Information about rooms can be found on the front page. There is required reading attached for each lecture which you are expected to read before the lecture. Lectures will assume you have read the literature and will not repeat it.

Examination form

The course consists of two assessment components, each of which must be passed in order to pass the course. The course is graded pass/fail (G/U).

1. Written assignments and quizzes:

  • The course has 6 mandatory weekly assignments and 2 quizzes (4.0 credits), all of which must be passed to pass this module.
  • An assignment is considered a pass if you have obtained at least 70% of the maximum score.
  • A quiz is considered a pass if you have obtained at least 60% of the maximum score.
  • All assignments are to be done and submitted in groups of two students, and you need to select your group within CodeGrade.
    • Exceptions may be requested from the Examiner in the first two weeks of the course only, but will only be granted under exceptional circumstances.
    • Assignments are released at the end of each module, with the initial deadline one week AFTER initial release.
    • Most assignments are returned as Jupyter notebooks on CodeGrade. Some parts of the assignment will be automatically graded, which means you will get immediate feedback for those parts. You will receive feedback and grading one week after the initial deadline for all assignments for the manually graded component.
    • Re-submissions do not receive feedback, only grading.
  • Quizzes are to be done and submitted individually. No exceptions.
    • The quizzes are multiple-choice Canvas quizzes. You have a maximum of 2 attempts for completing the quiz with a passing grade (the first submission, and the re-submission).
    • There is a 30-minute time limit for the Canvas quizzes.
    • For quizzes, automated item-level feedback may appear per Canvas settings after each attempt. You may also come to lab sessions to ask questions before using up your second attempt.
    • If you fail the quiz by the first submission deadline, you have one more week to try again with your second attempt.
  • If you do not pass each assignment/quiz, you can resubmit once. The re-submission deadline is 1 week after you receive your grades and listed above in the Syllabus. After this, the assignment/quiz is closed.
  • All deadlines are hard. Late submissions are considered re-submissions (and thus receive no manual feedback).
  • If you fail the re-submission, you will need to complete that assignment/quiz again in a later instance of the course.
  • Extensions are only given for valid reasons such as illness, serious family issues, etc.—not for holidays or university trips.
    • To request an extension, you are required to email the Examiner BEFORE the initial deadline (noon before assignment is due at latest).
    • Do not assume that if you request an extension, that it will be granted.
  • The use of generative AI tools (e.g., ChatGPT, DeepSeek, Claude, co-pilots) in carrying out assignments, unless where specifically instructed to use them (e.g., Module 7 Assignment: RAG with Ollama), is strictly prohibited and is considered plagiarism in this course. The use of generative AI to write-up assignments is also forbidden and considered plagiarism.

2. Hall exam:

  • The course has 1 mandatory digital hall exam (3.5 credits). The exam is graded pass/fail.
  • Exam date: Tuesday, October 27th, 2026. The exact time and location will be announced at a later date.
    • If you cannot make the above exam date, or if you fail the exam, then you may take the exam in another course instance.

  • Grading Structure:

    • The exam has 48 questions (6 questions per module), each worth a maximum of 1 point.

    • A total of 29 points (corresponding to roughly 60% of the total) is required to pass ("Approved" in Inspera).

    • The exam consists of a mixture of multiple-choice and multiple-response questions. Unanswered questions give 0 points.

      • Multiple-choice questions: A correct answer gives 1 point, and an incorrect answer gives 0 points.

      • Multiple-response questions: These always have at least one correct answer. If a question has n alternatives, of which k are correct, then each correctly chosen alternative gives 1/k points, and each incorrect alternative gives -1/(n-k) points. The overall points for the question is the sum of the points for the answers, with a minimum of 0 points.

  • The examination will be conducted as a digital examination via Inspera. It will be a multiple-choice exam and automatically graded.
  • Only a dictionary is allowed for this exam, nothing else.
  • The use of generative AI tools (e.g., ChatGPT, DeepSeek, Claude, co-pilots) in the exam is strictly prohibited and is considered plagiarism in this course.

Students with approved accommodations should contact the examiner and the examiner will coordinate suitable arrangements.

 

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 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.

The use of generative AI tools (e.g., ChatGPT, DeepSeek, Claude, co-pilots, etc.) in preparing assignments/quizzes is considered plagiarism.

Cases where plagiarism is spotted will be deferred to the disciplinary committee of the university and may lead to suspension.

 

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
  • 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
  • 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
  • show a reflective attitude in all learning

 

Re-examination instructions

Students re-taking the course from a previous failed instance of the course should consult the instructions for re-examination.

 

Link to the syllabus on Studieportalen: DAT566, DIT408