Applied Data Science is a Master's programme that aims to increase the ICT competence of engineers from different fields.
4 years/8 semesters
August every year
The programme is offered as a 2-year full-time study and as a part-time study over 4 years.
This part-time study can be taken in combination with work or other activities for those who live in the region and can follow the educational plan provided. Courses are taken together with full-time students who complete the degree in two years, but as a part-time student you will have fewer courses per semester, distributed over four years. The part-time study takes place during the day and most of the courses are based on laboratory work and project work in groups with compulsory attendance. Lectures are usually not streamed, but books and other materials cover the syllabus. You will usually need 1-2 days per week (depending on the semester) to follow mandatory activities.
Applied Data Science students will learn to extract relevant information from a compilation of large datasets from different sources. The ability to create, manage and exploit data has become one of the most important challenges for practitioners in most disciplines, sectors, and industries.
Students with expertise in Applied Data Science will be highly sought after in the future labor market, where they will contribute to the development of smart solutions and digitization.
The new study comes in the wake of Big Data and the need to find useful information in them. Analysis, understanding, and use of these data require interdisciplinary skills and knowledge. Applied Data Science creates opportunities for students from different engineering backgrounds who want to focus more on digitization and data analysis.
For students with a bachelor's degree in computer science or similar, we recommend the master's program in Computer Science, specialisation Data Science.
Applied Data Science is crucial for creating smart solutions, such as the development of smart cities, smart energy, smart healthcare. Applied Data Science specialises in information retrieval, data recovery and further in-depth study of statistics.
The Master's programme has a practical profile, where after specialisation in statistics and computer science you use algorithms on real data sets.
UiS has a strong research focus on analysis of large datasets, Cloud solutions, and machine learning.
University of Stavanger offers and encourages the student to take a preparatory summer course in programming and system administration. The first part is web-based and taken in June/July and the second part is 5 days on-campus in the first half of August. More information about the summer course: Preparatory course offer.
UiS is offering break rooms, reading rooms and computer rooms especially reserved for the master students. There is also a student organization for these students, ISI, which is arranging both academic and social gatherings.
You may also be interested in:
Computer Science, Data Science master
Computer Science, reliable and secure systems master
Fullført mastergrad i Applied Data Science gir grunnlag for opptak på PhD-studier innen informasjonsteknologi, matematikk og fysikk.
All study programmes at the UiS have a set of defined learning targets. Read more about the learning outcome for this study programme.
En kandidat med fullført og bestått 2-årig mastergrad i Applied Data Science skal ha følgende samlede læringsutbytte definert i form av kunnskap, ferdigheter og generell kompetanse:
K1: Avansert kunnskap innenfor data science, som omfatter databehandling, stordata, maskinlæring, datautvinning, statistikk og typiske programmeringsspråk for området, i.a.Python og R.
K2: Spesialisert innsikt i data analyse.
K3: Inngående kunnskap om vitenskapelige teori og metoder i data science.
K4: Anvende kunnskap om algoritmer for statistisk analyse, maskinlæring eller datautvinning på nye områder innenfor data science.
K5: Analysere faglige problemstillinger med utgangspunkt i fjerde vitenskapens paradigme, 4Vene av stordata (volum, velositet, variasjon, variabilitet), data-drevet tilnærming, CRISP-DM (cross-industry standard process for data mining).
F1: Analysere og forholde seg kritisk til ulike informasjonskilder, datasett og dataprosesser; og anvende disse til å strukturere og formulere data-drevet resonnement.
F2: Analysere eksisterende teorier, metoder og fortolkninger innenfor fagområdet og arbeide selvstendig med å anvende og evaluere ulike lagrings- og databehandlingsteknologier.
F3: Bruke CRISP-DM og vitenskapelige metoder for å utvikle dataanalyseprogrammer på en selvstendig måte.
F4: Gjennomføre en selvstendig, avgrenset datainnsamling, analyse og evaluering etter etablerte ingeniørprinsipper i tråd med gjeldende forskningsetiske normer.
G1: Analysere relevante etiske problemstillinger som følger av bruk av data og datautvinning.
G2: Anvende sine kunnskaper og ferdigheter på nye områder for å gjennomføre avanserte arbeidsoppgaver og prosjekter relatert til databehandling, dataanalyse og optimalisering.
G3: Formidle resultater av omfattende dataanalyse og utviklingsarbeid, og beherske Data Science-uttrykksformer.
G4: Kommunisere om problemstillinger, analyser og konklusjoner relatert til data-drevet forskning og utvikling, både med spesialister og til allmennheten.
G5: Bidra til nytenking og i innovasjonsprosesser ved å innføre data-drevet tilnærming.
A Bachelor's degree in engineering or equivalent is required.
The degree must include at least:
- 10 ECTS credits in informatics/computer sciences/an introductory course for engineers including programming
- 30 ECTS credits in mathematics/statistics/calculus
Admission to this master's programme requires a minimum grade average comparable to a Norwegian C (according to ECTS Standards) in your bachelor's degree.
If you have completed studies/courses outside the University of Stavanger, you must upload course descriptions that have clearly defined curriculum (learning outcomes). The course names and codes on the course descriptions must match the transcript of records. If you do not provide course descirptions, you might risk your application to not be prioritized.
Only Nordic citizens and applicants residing in Norway can apply to MSc in Applied Data Science.
Application period: 1 February - 20 April (only in 2022, 15 April is regular deadline)
(NB! The deadline for applicants with foreign educational background is 1 March.)
Frequently asked questions
What is the main difference between the three master's programs?
The difference between ADS (Applied Data Science) and DS (Data Science) is that in ADS, students receive more basic training in programming and databases that students in DS (and CS (Computer Science)) already have. Otherwise, there is a large overlap between ADS and DS. DS and CS have the same admission basis, while ADS has a different admission basis.
What is the difference between ADS / DS and CS (Reliable and secure systems)?
Specialization: Reliable and secure systems
The specialization in reliable and secure systems teaches you the management, design and programming of computer systems. The ability to integrate knowledge and skills in security, reliability and scalability together with algorithm theory and statistics is necessary to respond to challenges in computer systems in all industries.
Specialization in reliable and secure systems provides a basis for work with the development and planning of commercial computer systems for various purposes. You gain knowledge and skills in network security, reliability of distributed systems, simulation and modeling.
What do you learn in the specialization Data Science?
The specialization in Data Science teaches you to extract relevant information from a compilation of large data sets from different sources. The ability to create, manage and utilize data has become one of the most important challenges for practitioners in almost all disciplines, sectors and industries.
Specialization in Data Science provides a basis for work in data analysis and development of data processing systems for the entire data life cycle. You gain knowledge and skills in advanced statistics, data mining, machine learning and processing large amounts of data. The study will be highly sought after in the future labor market, with the development of smart solutions such as in smart cities, with smart energy and digitalisation.
By going on exchange to one of our partner institutions abroad as part of your studies, you will have an opportunity to get a unique education. In addition to improving your career opportunities, you grow as a person and gain the ability to greater reflect on the topics you study as part of your degree. All about exchange
Alle studenter ved UiS skal ha muligheten til å ta deler av studiet sitt i utlandet. For studenter på deltidsprogram kreves det gjerne noen ekstra tilpasninger.
For utfyllende informasjon om opplegg, anbefalinger og kontaktperson, se Applied Data Science master – heltid.
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Department of Electrical Engineering and Computer Science
Welcome to the Department of Electrical Engineering and Computer Science!
The department is responsible for teacing, research and development within electrical and computer engineering at UiS. As computer technology becomes increasingly important in society, so this department keeps growing in size and expanding its activities.
The department of Electrical Engineering and Computer Science offers a growing range of studies, from bachelor to Phd level (see full list on our Norwegian pages).
The master programme in Computer Science is international and taught in English.
Research in the department is concentrated on four main areas - follow the links to our Research pages for more information:
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