Course syllabus

Course-PM

DAT566 / DIT408 - Introduction to Data Science and AI - LP4 HT26 (7.5 hp)

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

 

Contact details

Lecturer/Examiner: Assist. Professor Stefano Sarao Mannelli  <s.saraomannelli@chalmers.se>
Lecturer: Assoc. Professor Moa Johansson <moa.johansson@chalmers.se>
Lecturer: Dr. Oana Geman <geman@chalmers.se>
Lecturer: Dr. Bastiaan Bruinsma <sebastianus.bruinsma@chalmers.se>
Responsible for grading and general questions:  Chenxiao Ma <mache@chalmers.se>
Responsible TA for Module 1 Filip Kronström <filipkro@chalmers.se>
Responsible TA for Module 2

Georg Kyhn

 <merila@chalmers.se>
Responsible TA for Module 3 Hang Zou <hangzo@chalmers.se>
Responsible TA for Module 4
Responsible TA for Module 5 Miaowen Dong

<miaowen@chalmers.se>
Responsible TA for Module 6 Mathis Rost <mathisr@chalmers.se>
Responsible TA for Module 7 Longde Huang <longde@chalmers.se>
Responsible TA for Module 8

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 Links to an external site.

Generally, the course includes 2 lectures (hybrid) and 2 laboratory sessions (hybrid) each week, except around holidays.

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

Mon 23 Mar 08:00

Wed 01 Apr 16:59

 Wed 15 Apr 16:59

Module 2 Assignment

Apartments

Mon 30 Mar 08:00

Wed 08 Apr 16:59

Wed 22 Apr 16:59

Module 3 Assignment

Diabetes

Mon 13 Apr 08:00

Wed 22 Apr 16:59

Wed 06 May 16:59

Modules 1-4 Quiz

Quiz 1

Mon 20 Apr 08:00

Wed 29 Apr 16:59

Wed 13 May 16:59

Module 5 Assignment

Seeds

Mon 27 Apr 08:00

Wed 06 May 16:59

Wed 20 May 16:59

Module 6 Assignment

Fashion MNIST

Mon 04 May 08:00

Wed 13 May 16:59

Wed 27 May 16:59

Module 7 Assignment

RAG with Ollama

Mon 11 May  08:00

Fri 22 May 16:59

Fri 05 Jun 16:59

Modules 5-8 Quiz

Quiz 2

Mon 18 May 08:00

Wed 27 May 16:59

Fri 05 Jun 16:59

All times/dates are in Gothenburg, SE time. 

 

The final exam's date is June 1st, and assess your mastery of the course content spanning all 8 modules. Logistical details on the exam (room number, time) will come later and be updated here. Re-examination will be on August 28th.

 

Lab sessions

Lab sessions will be held in a hybrid format. TAs will be available to assist you in person or via Zoom. If you need help, we recommend attending in person.

Information about rooms can be found on the home page. If you need to use Zoom, this Google Document page Links to an external site. is used as waiting list.

When you create a help request, first create a Zoom room and put the room id (the 10 or 11 digit number) as your name. A TA will then join the room. Do not enable a password for the room.

 

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 hybrid, in-person and over Zoom. 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/quiz is considered a pass if you have obtained at least 70% 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 (e.g., LP1).
  • 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.
  • The exam will be multiple choice and considered a pass if you obtain at least 70% of the maximum score.
  • Exam date: TBD (4 h).
    • If you cannot make the above exam date, or if you fail the exam, then you may take the exam in another course instance.
  • The examination will be conducted as a digital examination via Inspera. It will be a multiple-choice exam and automatically graded.
  • No aids will be permitted during the examination (e.g., no markings, index cards, or notes).
  • 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 Links to an external site., DIT408 Links to an external site.