Mann foran pc.
Duration:3 years

Data Science

This programme is for those who are passionate about the IT discipline and who want a career within an exciting field in growth
Mann foran pc.

Key information

  • Motivating environment

    The study program consists of small classes and lecturers who makes sure that all students are cared for, seen and heard.
  • Exchange program

    You can apply to our exchange program and travel to Finland, South Korea, Germany amongst others.
  • Bachelor
  • Fall 2024
  • Full-time
  • 180 Points
  • Oslo
  • 3 years
  • English

Learning Outcomes

The Bachelor of Data Science teaches you about fundamental topics within information technology as well as the latest research in the field. You will work with real cases from the industry, the latest IT tools and gain an in-depth understanding of subjects such as algebra, programming and IT regulations.

  • Information Risk and Security
  • Machine Learning
  • Big Data and Cloud Computing
  • Research Methods
  • Predictive Analytics

Following the completion of the course, you will have the tools needed in order to begin an exciting career within an industry in rapid growth. You will also have several options at the master's level, including the Master’s degrees in Information Technology at Kristiania University College.

Study model

This bachelor's degree lasts three academic years, and each academic year is divided into two semesters. Here you can see an overview of compulsory subjects and what opportunities you have for practice, exchange and specialization. We reserve the right to make changes.
180 total ECTS credits
60 ECTS credits
1. semester
2. semester

  • PGR206Data Structures and Algorithms

    The course will provide insight into algorithms and data structures that are central to the work of implementing and designing effective computer systems. Emphasis is placed on asymptotic analysis of worst-case scenarios, as well as central algorithms and data structures related to search and sorting. The course also deals with graph algorithms, optimization algorithms and data-compression algorithms.

  • PGR210Machine Learning and Natural Language Processing

    The course provides knowledge of the key concepts, techniques and methods related to machine learning. Topics include an understanding of the mathematical basics of data mining and machine learning, linear models for regression such as maximum likelihood, sequential learning, regularized least squares and classification models such as probabilistic generative models, probabilistic discriminative models. Furthermore, the course provides the students with practical hands-on experience on machine learning using open source machine learning libraries such as scikit-learn in Python programming language. The course also provides knowledge of the key concepts, techniques and methods in natural language processing to text analytics. The students gain in-depth knowledge of natural language processing and will further apply this to practical scenarios with acquired skills in text classification methods. The course provides students with hands-on experience on text analytics using open source machine learning libraries such as scikit-learn, Natural Language Toolkit (NLTK) in Python programming language. After completing the course, the students will be able to apply and use appropriate machine learning techniques in various data science domains.

  • PGR211Advanced Programming for Data Science

    The course will discuss different programming approaches in the Python programming language, especially, Object-oriented features vs functional programming practices and web programming techniques. The course will focus on when it is appropriate to use each of these features/approaches to address problems, not only limited to data science problems but also to understand Python's capabilities as a general-purpose programming language. The course will cover topics related to Object-oriented features of Python, Functional programming practices such as higher-order functions and anonymous functions, and web programming using some of the popular web frameworks in Python.

  • VALUTV99930EValgemner eller utveksling 30 SP
  • PG3302Software Design

    Emnet skal gjøre studentene i stand til å designe og videreutvikle større programvaresystemer i tråd med kjente teknikker for modellering, testing og implementasjon.

  • PGR207Deep Learning

    The course provides knowledge of the key concepts, techniques and methods related to artificial neural networks; deep learning. The students gains in depth knowledge of mathematical foundations of deep learning, neural networks and gains advanced skills in applying the appropriate tools, techniques and development of the respective areas. Furthermore, the course provides the students with practical hands-on experience on deep learning using open source deep learning libraries in the Python programming language. After completing the course, the students will be able to apply and use appropriate deep learning techniques within various data science domains.

  • PGR304Predictive Analytics

    The course provides knowledge of the key concepts, techniques and methods in predictive analytics. This course will cover methods and tools for data pre-processing for forecasting tasks in data science, techniques for selecting well-suited models for analysis, model performance evaluation tools. The course provides students with hands-on experience on predictive analytics using open source statistics tools. After completing the course, students will be able to apply and use various predictive analytics techniques such as regression, time series on numerical datasets.

  • PGR307Agile Project
  • BAO304Bachelorprosjekt
  • PGR306Research Methods

    The course aims to introduce research methods with a focus on methods that are especially relevant for the Data Science. The course supports the bachelor's degree project.

Career Opportunities

Meet the faculty

Bildet viser en mannlig foreleser som står foran en gruppe studenter i et klasserom. Han står ved en skjerm som viser ordene "Augmented Reality". Studentene, som hovedsakelig er sett bakfra, bruker bærbare datamaskiner, noe som antyder at de tar notater eller deltar aktivt i leksjonen. Klasserommet ser moderne ut, med lyse vinduer i bakgrunnen.
  • How we work

    Our research emphasizes economics, innovation, digitization and IT. In addition, there are several exciting research projects in applied informatics, information systems and human-computer interaction. Our lecturers have extensive experience both within industry and academia. Relevant working life experience is brought into the teaching through lecturing and guest lecturing. This will make you used to working life challenges and will ease the transition between study life and work life.

    Moreover, we collaborate with Gründergarasjen (Oslo’s incubator for technology-intensive early-stage startups) and Loftet Studentinkubator (Kristiania’s own incubator for students who have a business idea or a startup).

  • Campus Life

    Kristiania is a place for everyone, whilst being a place where you will study along with others who share your passions. An education from Kristiania is practice-oriented, which makes our students sought-after in the labour market, even before they have finished their education. The students' own line associations make for opportunities to meet and bond on the basis of shared interests, while the incubators like Loftet and Bryggeriet creates a bustling and inspiring environment for the students' own companies.

This is the application process

Here you will find important information about the application process and how you can best prepare for the start of your studies.
  • Important deadlines

    The application deadline is 15. April 2023. The documentation deadline for diplomas and certificates is 1. July 2023. Note that 15. April 2023 falls on a Saturday.
  • Processing time

    For study programs with rolling admissions, you will receive a conditional offer within 1–3 days of submitting the application, if there are available slots for the study program you applied for.
  • How to apply

    Min Side for søkere is where you accept the offer and upload necessary documentation for your qualifications.
  • Semester registration

    You must register and confirm your individual education plan before you are reported as an active student to the Norwegian State Educational Loan Fund, and to gain access to your subjects in Canvas, the learning platform.
  • SiO (Oslo) and Sammen (Bergen)

    SiO and Sammen offers housing, health services, kindergardens, fitness centers and much more to its members.
  • Loans and grants

    All our study programs are publicly approved and give the right to apply for loans and grants from the Norwegian State Educational Loan Fund (Lånekassen).
  • Services and adaptations

    As a student, you can get guidance, everyday adaptation and follow-up on study-related questions and challenges. We have a duty of confidentiality.
  • Mitt Kristiania

    This is where you get an overview of your schedule, syllabus, services and other tools you need as a student.
  • Student ID card

    As a new student, you can have a student card made on all our campuses except the Brenneriveien Campus. Your student card serves as an access card at the college’s campuses, ID for exams, payment card for printers and library card.

Do you have questions?

Our student ambassadors are happy to answer your questions - by chat, phone, email or video.
Bildelenke til veiledningsside.

Frequently asked questions