Data Science

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.

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.

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.

See elective courses or exchange opportunities.

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.

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.

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

The purpose of the course is to give the student an experience in mastering the whole of a project, with emphasis on applying a flexible method: Scrum. Scrum is a flexible process framework for developing innovative products and services, especially suitable for software development.

Through a process for developing a technical solution, the student will plan and implement a comprehensive project case for a company in a multidisciplinary group, and will receive training in using modern agile techniques and tools along the way.

The course aims to introduce research strategies and methods, which are especially relevant for the information technology sector. Research is a cyclical process where approaches are carefully adjusted and refined over time.

The aim of this course is to provide students with a fundamental understanding of research as a conceptual, empirical and practical approach to gathering new insight and knowledge within infomation technology. Teaching centres on applied research from the fields of information systems, data science and computer science and presents students with relevant methods from this domain, along with their possibilities and limitations.

The course supports the bachelor's degree project, primarily in relation to developing a research strategy, applying appropriate methods for data collection and reporting.

I dette emnet får studentene yrkeserfaring ved å gjennomføre et IT-prosjekt i en bedrift. Studentene skal demonstrere bred kunnskap om sentrale temaer og teorier, samt vise ferdigheter i metode, bruk av verktøy og teknologier innenfor fagområdet. Prosjektet gjennomføres i grupper og resultatet av arbeidet dokumenteres i en prosjektrapport. Prosjektleveransen defineres og utvikles i samråd med bedriften, samt at studentene følges opp av en intern veileder ved skolen. Dette emnet bygger også på tidligere emner i bachelorløpet, i form av blant annet kunnskap om utviklingsmetoder, risikohåndtering, prosjektarbeid og prosjektstyring. Utover dette må også studentene regne med å sette seg inn i ny kunnskap relatert til prosjektet de skal gjennomføre. Det kan være knyttet til bruk av programmeringsspråk, metode eller programvare. Emnet har en sterk arbeidslivsrelevans og studentene får reell og nyttig arbeidserfaring i løpet av prosjektperioden.