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.
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Study points: 10
Course coordinator:
Antorweep Chakravorty
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.
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Study points: 10
Course coordinator:
Aksel Hiorth
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.
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Study points: 10
Course coordinator:
Tore Selland Kleppe
Database Systems Year 1 / Semester 2
This course introduces students to fundamentals of database systems. The course includes basic database theory, data models, data modelling, relational database, SQL and transactions. The course teaches how to apply a database system and how to design a good database.
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Study points: 10
Course coordinator:
Antorweep Chakravorty
Data mining Year 1 / Semester 2
The purpose of this course is for students to gain knowledge and practical experience of data mining techniques. The lecture 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 methods to extract actionable knowledge. The course will provide the opportunity for students to learn state-of-the-art data mining algorithms and tools. The students will get hands-on experience to try these tools on real data.
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Study points: 10
Course coordinator:
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.
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Study points: 10
Course coordinator:
Trygve Christian Eftestøl
Master's thesis in Applied Data Science Year 2 / Semester 3

3rd semester at UiS or Exchange Studies

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.
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Study points: 10
Course coordinator:
Reggie Davidrajuh
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.
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Study points: 10
Course coordinator:
Krisztian Balog
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.
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Study points: 10
Course coordinator:
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.
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Study points: 10
Course coordinator:
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.
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Study points: 10
Course coordinator:
Rong Chunming
Project in Computer Science Year 2 / Semester 3
The project gives practice in solving a research assignment within the computer science area.
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Study points: 10
Course coordinator:
Leander Nikolaus Jehl
Exchange 3rd semester
Exchange Studies 3rd semester
Exchange - 30 SP Year 2 / Semester 3

This is the study programme for 2019/2020. It is subject to change.