Course syllabus

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Course-PM

DAT695 DAT695 Introduction to data science lp1 HT25 (7.5 hp)

Course is offered by the department of Computer Science and Engineering

Contact details

List of...

  • examiner
  • lecturer
  • teachers
  • supervisors

...along with their contact details. If the course have external guest lecturers or such, give a brief description of their role and the company or similar they represent.

If needed, list administrative staff, along with their contact details.

Course purpose

Short description of the course purpose and content: can be copied from syllabus in Studieportalen. Additional information can be added.

Schedule

TimeEdit

Course literature

List all mandatory literature, including descriptions of how to access the texts (e.g. Cremona, Chalmers Library, links).

Also list reference literature, further reading, and other non-mandatory texts.

Course design

Description of the course's learning activities; how they are implemented and how they are connected. This is the student's guide to navigating the course. Do not forget to give the student advice on how to learn as much as possible based on the pedagogy you have chosen. Often, you may need to emphasize concrete things like how often they should enter the learning space on the learning platform, how different issues are shared between supervisors, etc.

Provide a plan for

  • lectures
  • exervises
  • laboratory work
  • projects
  • supervision
  • feedback
  • seminars

Should contain a description of how the digital tools (Canvas and others) should be used and how they are organized, as well as how communication between teachers and students takes place (Canvas, e-mail, other).

Do not forget to describe any resources that students need to use, such as lab equipment, studios, workshops, physical or digital materials.

You should be clear how missed deadlines and revisions are handled.

Changes made since the last occasion

A summary of changes made since the last occasion.

Learning objectives and syllabus

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, including diversity, equality, and inclusion.
  • 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/DAT695/?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

Description of how the examination – written examinations and other – is executed and assessed.

Include:

  • what components are included, the purpose of these, and how they contribute to the learning objectives
  • how compulsory and/or voluntary components contribute to the final grade
  • grading limits and any other requirements for all forms of examination in order to pass the course (compulsory components)
  • examination form, e.g. if the examination is conducted as a digital examination
  • time and place of examination, both written exams and other exams such as project presentations
  • aids permitted during examinations, as well as which markings, indexes and notes in aids are permitted

Do not forget to be extra clear with project assignments; what is the problem, what should be done, what is the expected result, and how should this result be reported. Details such as templates for project reports, what happens at missed deadlines etc. are extra important to include.

Course summary:

Date Details Due