Applied Data Science, Master of Science Degree Programme
Study programme description for study year 2022-2023
Credits (ECTS)
120
Studyprogram code
M-APPDAT
Level
Master's degree (2 years)
Leads to degree
Master of Science
Full-/Part-time
Full-time
Duration
4 Semesters
Undergraduate
No
Language of instruction
English
A master's degree in Applied Data Science makes you eligible for the most demanding and interesting work tasks within data analysis, smart solutions (such as smart cities, smart energy), and digitization. A master's degree in Applied Data Science makes you eligible for the most demanding and interesting work tasks within data analysis, smart solutions (such as smart cities, smart energy), and digitization.
Programme content, structure and composition
After the student has been admitted to the two-year master's programme in Applied Data Science, the student must take a test in programming and system administration. If the student does not pass the test, UiS will offer and encourage the student to complete a preparatory summer course in programming and system administration. The purpose of the course is that the students should be best prepared for the master's programme. The course takes place in early August before the regular semester starts.
The University of Stavanger does not consider it necessary to offer summer courses for those students who have already passed the following courses at the University of Stavanger:
-10 ECTS in programming and at least 5 ECTS in operating systems.
The study programme has practical courses that build on mathematics, statistics and fundamental programming from the bachelor's program in engineering or science. The study programme consists of advanced statistic and algorithm courses, machine learning and databases. Students can further specialize in information retrieval, data mining and theoretical statistics. The two-year master's programme in Applied Data Science comprises 120 ECTS.
Learning outcomes
After having completed the master’s programme in Applied Data Science, the student shall have acquired the following learning outcomes, in terms of knowledge, skills and general competences:
Knowledge
K1: Advanced knowledge within Data Science, which includes data processing, data, machine learning, data extraction, statistics and typical programming languages for the area, including: Python and R.
K2: Specialized insight into data analysis.
K3: In-depth knowledge of scientific theory and methods in Data Science.
K4: Apply knowledge about algorithms for statistical analysis, machine learning or data extraction in new areas within data science.
K5: Analyse professional issues based on the fourth science paradigm, 4Vs of big data (volume, velocity, variety, and variability), data-driven approach, CRISP-DM (cross-industry standard process for data mining).
Skills
S1: Analyse and relate critically to different sources of information, datasets and data processes; and apply these to structure and formulate data-driven reasoning.
S2: Analyse existing theories, methods and interpretations within the subject area and work independently in applying and evaluating different storage and data processing technologies.
S3: Use CRISP-DM and scientific methods to develop data analysis programs in an independent way.
S4: Conduct independent, limited data collection, analysis and evaluation according to established engineering principles in accordance with current research ethical standards.
General Competence
G1: Analyse relevant ethical issues arising from data usage and data recovery.
G2: Apply their knowledge and skills in new areas to carry out advanced tasks and projects related to data processing, data analysis and optimisation.
G3: Communicate results of comprehensive data analysis and development work, and master Data Science expressions.
G4: Communicate on issues, analyses and conclusions related to data-driven research and development, both with specialists and to the general public.
G5: Contribute to new ideas and innovation processes by introducing data-driven approaches. comprehensive data analysis and development work, and master Data Science expressions.
Career prospects
With a master's degree in Applied Data Science, you are in demand in almost all industries, and this study opens up for many different types of jobs. You can work in an IT consulting company, telecommunications company, energy company, health trust, in another public sector or in a technology development company that requires knowledge and insight into the handling and analysis of large data sets. The study is highly sought after in the labor market of the future, with the development of smart solutions such as in smart cities, with smart energy and digitalisation.
Completed master’s degree in Applied Data Science provides the basis for admission the PhD programme in Information technology, mathematics and physics.
Course assessment
Schemes for quality assurance and evaluation of studies are stipulated in the Quality system for education
Study plan and courses
Enrolment year:
-
Compulsory courses
-
APPMAS: 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
-
DAT530: Discrete Simulation and Performance Analysis
Year 2, semester 3
-
DAT640: Information Retrieval and Text Mining
Year 2, semester 3
-
STA500: Probability and Statistics 2
Year 2, semester 3
-
STA530: Statistical Learning
Year 2, semester 3
-
-
Other electives 3rd semester
-
DAT510: Security and Vulnerability in Networks
Year 2, semester 3
-
DAT620: Project in Computer Science
Year 2, semester 3
-
ELE510: Image Processing and Computer Vision
Year 2, semester 3
-
ELE680: Deep Neural Networks
Year 2, semester 3
-
-
-
Exchange 3rd semester
-
Exchange Studies 3rd semester
-
-
-
Compulsory courses
-
DAT540: Introduction to Data Science
Year 1, semester 1
-
MOD510: Modeling and Computational Engineering
Year 1, semester 1
-
STA510: Statistical modeling and simulation
Year 1, semester 1
-
DAT220: Database Systems
Year 1, semester 2
-
DAT550: Data Mining and Deep Learning
Year 1, semester 2
-
ELE520: Machine Learning
Year 1, semester 2
-
APPMAS: 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
-
DAT530: Discrete Simulation and Performance Analysis
Year 2, semester 3
-
DAT640: Information Retrieval and Text Mining
Year 2, semester 3
-
STA500: Probability and Statistics 2
Year 2, semester 3
-
STA530: Statistical Learning
Year 2, semester 3
-
-
Other electives 3rd semester
-
DAT510: Security and Vulnerability in Networks
Year 2, semester 3
-
DAT620: Project in Computer Science
Year 2, semester 3
-
ELE510: Image Processing and Computer Vision
Year 2, semester 3
-
ELE680: Deep Neural Networks
Year 2, semester 3
-
-
-
Exchange 3rd semester
-
Exchange Studies 3rd semester
-
-