- Compulsory courses
- Introduction to data science Year 1 / Semester 1
The course will provide a knowledge and experience in data engineering tasks and will accustom students with data science project lifecycle.
Read more about Introduction to data scienceStudy points: 10
- Modeling and Computational Engineering Year 1 / Semester 1
This course introduces numerical methods and modeling techniques used to solve practical problems. The course provides insights and skills in computational thinking and programming techniques
You will learn the most common numerical methods used to solve complex physical, biological, financial or geological phenomena. Examples of methods are numerically derivation, numerical integration, Monte Carlo and boot strapping methods, inverse methods, numerical solution of common differential equations, simulated annealing, and colony optimization, lattice Boltzmann models, random walk models, box (compartment) models.
The primary programming language is Python. Through assignments, you will learn how to set up mathematical models of a phenomenon, develop algorithms, implement them, and investigate the strength and limitations of the solution method and the mathematical model.
Read more about Modeling and Computational EngineeringStudy points: 10
- Probability and Statistics 2 Year 1 / Semester 1
Basic issues in probability. Presentation of a number of commonly used probability distributions. Short introduction to extreme-value statistic. Estimation, in particular the maximum likelihood principle,and confidence intervals in various situations. Brief introduction to Bayesian statistics.Stochastic processes, in particular Poisson processes and Markov processes. Theory and areas for applications of the various methods will be covered.
Read more about Probability and Statistics 2Study points: 10
- Database Systems Year 1 / Semester 2
This course introduces students to the fundamentals of database systems. The course includes basic database theory, data models, data modeling, relational database, SQL and transactions. The course teaches how to apply a database system and how to design a good database.
Read more about Database SystemsStudy points: 10
- Data Mining and Deep Learning Year 1 / Semester 2
The purpose of this course is for students to gain knowledge and practical experience of data mining and deep learning techniques. The course will prepare the students with a deep knowledge of technologies and be able to prepare large-scale data for data mining (pre-processing) and use a number of data mining and deep learning methods to extract actionable knowledge. The course will provide the opportunity for students to learn state-of-the-art data mining and deep learning algorithms and tools. The students will get hands-on experience to try these tools on real data.
Read more about Data Mining and Deep LearningStudy points: 10
Vinay Jayarama Setty
- Machine learning Year 1 / Semester 2
The course focuses on methods for learning the underlying structures from data and to train models that can make predictions when presented with new data. Such predictions can typically involve the discrimination between different categories of data, or pattern classification, which will be the main focus of this course.
Read more about Machine learningStudy points: 10
Trygve Christian Eftestøl
- Master's thesis in Applied Data Science Year 2 / Semester 3
The master thesis is an independent project in which you will apply the knowledge acquired during your studies on solving a given assignment. It is through this assignment that you will show your abilities and qualities as a coming engineer.
The assignment will normally be carried out during the last semester of your studies. At this stage you will have acquired the knowledge and know-how needed for accomplishing a relevant assignment in your studies.
Read more about Master's thesis in Applied Data ScienceStudy points: 30
- Courses at UiS 3rd semester
- Recommended electives 3rd semester
- Discrete Simulation and Performance Analysis Year 2 / Semester 3
This course first introduces Petri net theory; then, the theory is used for modeling, simulation and performance analysis of discrete event systems.
Read more about Discrete Simulation and Performance AnalysisStudy points: 10
- Information retrieval and text mining Year 2 / Semester 3
The course offers an introduction to techniques and methods for processing, mining, and searching in massive text collections. The course considers a broad variety of applications and provides an opportunity for hands-on experimentation with state-of-the-art algorithms using existing software tools and data collections.
Read more about Information retrieval and text miningStudy points: 10
- Statistical modeling and simulation Year 2 / Semester 3
This course provides a foundation for problem solving in technology, science and economy using statistical modeling, simulation and analysis.
Read more about Statistical modeling and simulationStudy points: 10
Stein Andreas Bethuelsen
- Statistical learning Year 2 / Semester 3
Introduction to statistical learning, multiple linear regression, classification, resampling methods, model selection, regularization, non-linearity, tree-based methods, cluster analysis.
Read more about Statistical learningStudy points: 10
Jan Terje Kvaløy
- Other electives 3rd semester
- Security and Vulnerability in Networks Year 2 / Semester 3
Basic problems and challenges, cryptography, cryptographic protocols, secure software and malicious code, access control, network security, security assessment and management, regulations and laws. Guest lectures give relevance to best practice.
Read more about Security and Vulnerability in NetworksStudy points: 10
- Project in Computer Science Year 2 / Semester 3
The project gives practice in solving a research assignment within the computer science area.
Read more about Project in Computer ScienceStudy points: 10
Leander Nikolaus Jehl
- Exchange 3rd semester
- Exchange Studies 3rd semester
- Exchange - 30 SP Year 2 / Semester 3
This is the study programme for 2020/2021.