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BSc · 180 ECTS · English · IT University of Copenhagen

BSc in Data Science

Every course on the ITU curriculum for this programme, by semester, the way the university publishes it. Student jobs matched to these courses appear here as they are indexed.

17 courses indexed2 curriculum versionsTaught in English
Programme structure on itustudent ↗Official programme page ↗Curriculum (PDF) ↗

Curriculum

180 ECTS across 6 semesters
Curriculum version · pick your enrolment year

1st semester · Autumn

30 ECTS

2nd semester · Spring

30 ECTS
  • Applied StatisticsPrerequisite: Introduction to Data Science and Programming · Linear Algebra and Optimisation · Foundations of Probability
    15 ECTS
  • Algorithms and Data StructuresPrerequisite: Introduction to Data Science and Programming
    7,5 ECTS
  • Projects in Data SciencePrerequisite: Introduction to Data Science and Programming · Linear Algebra and Optimisation · Foundations of Probability
    7,5 ECTS

3rd semester · Autumn

30 ECTS
  • Machine LearningPrerequisite: Introduction to Data Science and Programming · Linear Algebra and Optimisation · Projects in Data Science
    15 ECTS
  • Introduction to Database SystemsPrerequisite: Introduction to Data Science and Programming
    7,5 ECTS
  • Network AnalysisPrerequisite: Introduction to Data Science and Programming · Linear Algebra and Optimisation · Applied Statistics
    7,5 ECTS

4th semester · Spring

30 ECTS

5th semester · Autumn

30 ECTS

6th semester · Spring

30 ECTS
Notes from the curriculum
  • Up until Autumn 2025 this course was called Software Development and Software Engineering

What a graduate can do

The programme's own objectives, as written in the ITU curriculum in force from admission 2023. This is the language employers will read your coursework in.

Knowledge and understanding
  • have research-based knowledge of theory, methodology and practice within data- science specific areas of mathematics: optimisation, machine learning, statistical analysis, network analysis, experiment design, and algorithms.
  • have research-based knowledge of theory, methodology and practice within data- science specific areas of computing: programming languages, query languages, databases, data processing, and large-scale data analysis.
  • have research-based knowledge of social science, be able to reflect on relevant social theories.
  • be able to understand and reflect on theories, scientific methodologies and practice of the above areas.
  • be able to understand and reflect on existing data and analytical software-platforms and their adequacy to specific data science problems.
  • be able to consider data within a global perspective, with respect to different contexts and cultures.
Skills
  • be able to develop software in a general-purpose programming language.
  • be able to implement scalable algorithms for fundamental data analysis tasks (e.g. in machine learning, statistical inference), based on technical descriptions in textbooks or the research literature.
  • be able to evaluate theoretical issues of problems in order to select and apply appropriate machine learning methods, algorithms, and software tools to perform data analysis, statistical inference, or predictive analytics tasks, based on scalability and performance.
  • be able to apply systems for data management in order to clean, transform, and query data.
  • be able to carry out adequate empirical evaluation of model performance in order to ensure optimal accuracy.
  • be able to organise, summarise, and visualise data and the outcomes of inferential processes for relevant stakeholders.
  • be able to communicate the academic issues associated with inferential processes and computational requirements to relevant stakeholders.
  • be able to assess the level of privacy and security ensured by a technical solution and communicate it in a way that is understandable to non-specialists, both from a global perspective as well as within a particular cultural context.
Competences
  • be able to independently and collaboratively dissect complex situations in order to identify the domain knowledge necessary for robust data processing and interpretation of outcomes, and to plan data collection on which robust statistical conclusions can be made.
  • be able to independently and collaboratively develop domain-specific data cleaning, statistical models, and select algorithms and analysis and machine learning, based on input from domain experts.
  • be able to articulate the importance of data origins, data collection context and impacts of inferential processes as part of reporting of findings.
  • be able to independently and collaboratively consider the relevant context of decision- making given available data, and critically assess implications of data analytics and of the inferential process in a domain, including privacy and ethical concerns.
  • be able to maintain and develop professional competencies for global state-of-the-art technologies, and adapt to new domains of application.
  • be able to develop data-driven analyses and solutions from the bottom up, both independently and collaboratively.

Source: BDS-Curriculum2023-rev-2024-in-force-from-admission-2023-pdf.pdf

studyjob.ai is not affiliated with the IT University of Copenhagen. The official programme structure and curriculum on itustudent.itu.dk govern; this page reproduces course facts (titles, codes, ECTS, semesters, prerequisites) as collected on 10 September 2026 and links to the university's own course descriptions. Spotted an error? ops@studyjob.ai