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Foundations of Data Science

The course is an introductory course that prepares you for studies in the field of data science.

The course begins with the basics of data science, including methods for frequent itemsets mining, clustering, classification and deep learning. The course, therefore, provides a general conceptual framework for the subject area and introduces the terminology relevant to the more specialised data mining and machine learning courses offered in the program.

 

The course provides an introduction to the basic concepts and techniques that are essential to understand and practice the field of data science. The course begins with an overview of the data science field and also covers the mathematical fundamentals that are essential for understanding data science algorithms
and methods. The course also covers data science frameworks for structured data analysis and methodologies for data preparation.

The course covers supervised and unsupervised learning, including a wide range of topics such as association rules, dimensionality reduction, clustering, regression and classification, giving students a solid understanding of predictive and descriptive modeling methods.

The course also goes into advanced topics such as neural networks and reinforcement learning and shows the breadth of applications in the data science field.

Furthermore, the course provides an overview of current research in the field and highlights the importance of keeping up to date with the latest advances and methods.

Overall, this course equips students with the foundational knowledge and practical skills required to effectively navigate the multifaceted field of data science.


Teaching Format

The teaching consists of lectures and exercises with tutoring.
The teaching takes place in English.


Assessment

The course is examined through:

  • on-campus written exam and
  • assignments.

Examiner


The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.
Note that the course literature can be changed up to two months before the start of the course.


Course reports are displayed for the three most recent course instances.








Study counsellors

Margrét Håkansson and Mitra Wijkman

master@dsv.su.se
+468161686

Visiting hoursPlease contact us via email if you want to book a meeting. We are available on Campus in Kista and via Zoom.

Phone hoursThursday 12.30–2 pm

Irregular office hoursLast phone hours for autumn 2025: Thursday 11 December
First phone hours for spring 2026: Thursday 15 January