TDSR22 - Data Science

Course Code:	TDSR22
Confirmed:	Aug 20, 2025
Valid From:	Aug 31, 2026

Education Cycle:	Second-cycle level
Disciplinary domain:	Technology
Subject group:	Computer Technology
Specialised in:	A1N Second cycle, has only first-cycle course/s as entry requirements
Main field of study:	Computer Science

Intended Learning Outcomes (ILO)

On completion of the course the student shall:

Knowledge and understanding

display knowledge of the phases in a data analysis project
display knowledge of the fundamental tasks in data analysis
show familiarity with key research directions in data analysis and machine learning

Skills and abilities

demonstrate the ability to identify situations or tasks for which data
demonstrate the ability to identify and apply a suitable technique based on a problem description
demonstrate the ability to collect, select and preprocess data in order to prepare it for data analysis
demonstrate the ability to use a software tool for all parts of a data analysis project
demonstrate the ability to analyse results and models from a data analysis project

Judgement and approach

demonstrate an understanding of how data analysis can be used as a tool to support business and technical decision-making

Content

The exponential growth of the digital universe, particularly in the form of storage and computing power in recent decades, enables companies to accumulate huge amounts of data at moderate cost. Accompanying this technological shift is a widespread realization that collected data contain potentially valuable information. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, through data analysis.

The course includes the following elements:

Organization of a data analysis project and its different phases, i.e., business understanding, data understanding, preprocessing, modelling, evaluation and deployment
Understanding and preparing data for analysis
Fundamental tasks in data analysis, i.e., classification, regression, clustering, association analysis and deviation analysis
Software tools for data analytics
Basic machine learning techniques for finding patterns, explanations and predictions
Data analytics applied in different engineering and business domains

Type of instruction

The course consists of lectures, exersices, assignment and project with tutoring.

Language of instruction is in English.

Entry requirements

The applicant must hold the minimum of a bachelor’s degree (i.e the equivalent of 180 ECTS credits at an accredited university) with at least 90 credits in Computer Engineering, Computer Science, Informatics, Information Systems, Information Technology or Electrical Engineering (with relevant courses in computer engineering), or equivalent, or passed courses at least 150 credits from the programme Computer Science and Engineering. The bachelor’s degree should comprise a minimum of 15 credits in mathematics. Proof of English proficiency is required.

Examination and grades

The course is graded 5, 4, 3 or U.

Registration of examination:

Name of the Test	Value	Grading
Examination ¹	2.5 credits	5/4/3/U
Assignments	1.5 credits	G/U
Project	3.5 credits	G/U

¹Determines the final grade of the course, which is issued only when all course units have been passed.

Course literature

Please note that changes may be made to the reading list up until eight weeks before the start of the course.

Principal text:

Title: Guide to Intelligent Data Science
Author(s): Berthold, Borgelt, Höppner, Klawonn, & Silipo (2020)
Publisher: Springer
ISBN: 978-3-030-45573-6 (available online through library services)

Additional texts:

3-5 additional research articles and technical reports

COURSE SYLLABUS