Motivating environmentThe study program consists of small classes and lecturers who makes sure that all students are cared for, seen and heard.
Exchange programYou can apply to our exchange program and travel to Finland, South Korea, Germany amongst others.
Guest lecturersThe study program has guest lecturers with relevant experience from the industry.
Admission requirementsFor this study program it is required that you have General University and College Admissions Certification. It is also required to have passing grade in mathematics R1 or S1+S2.
Tuition fees52 800 kr per semester*. The study programme qualifies for grants and loans from the Norwegian State Educational Loan Fund (Lånekassen).
- 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.
- 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
- 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.
Meet the faculty
How we workOur 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.
Campus LifeKristiania 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
Important deadlinesThe 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.Read more
Processing timeFor 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.Read more
Semester registrationYou 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.Read more
SiO (Oslo) and Sammen (Bergen)SiO and Sammen offers housing, health services, kindergardens, fitness centers and much more to its members.Read more
Services and adaptationsAs a student, you can get guidance, everyday adaptation and follow-up on study-related questions and challenges. We have a duty of confidentiality.Read more
Mitt KristianiaThis is where you get an overview of your schedule, syllabus, services and other tools you need as a student.Mitt Kristiania
Student ID cardAs 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.Read more