Bachelor in Applied Data Science

IT education focusing on machine learning, artificial intelligence and the growing global need for big data analytics. Applied Data Science teaches you scientific methods for working with data in a practical and relevant way.

Highly sought after on the job market

Data science is about using data to analyze, create insights, and predict future events – and thus create major competitive advantages for companies. Data scientists are consequently becoming very attractive. The enormous need for – and lack of expertise – means that data scientists are often very well paid.

We are living in the data age! Netflix, Spotify, Facebook, Google, Amazon are all using huge amounts of data to stay ahead in their areas. So do smaller companies, where the possibilities are endless. Data comes from everywhere – online sales transactions, climate and traffic sensors, GPS, cell phone systems, transportation networks, industry systems, oil and gas, healthcare and the Internet of Things. This bachelor program teaches you scientific methods for working with data in a practical and relevant way.

The shortage of data scientists is becoming a serious constraint in some sectors.Harvard Business Review

Throughout the bachelor program students will learn the theoretical foundations required for working in this domain as well as the practical application of tools and techniques used in the field of data science. This includes data management, analytics and visualisation, software development and deployment, mathematical and statistical analysis, and artificial intelligence and machine learning.

Program Structure

The first year of the program has been designed to develop a breadth of foundation skills required by data scientists. During this year of study students will develop programming, mathematical, networking and data management skills alongside research and project management.

During the second year of study students will further develop their programming and software development skills. They will also explore statistical tools and techniques for data analysis and explore NoSQL data storage technologies.

In their final year students will gain practical experience in big data analytics and data visualisation, and develop applications using machine learning principles. This year also includes the opportunity to develop domain-specific practical expertise, exploring the data requirements of the industry sectors of Oil and Gas, Engineering and Information Technology, or society-related sectors of Government and Healthcare.

After completing the degree, graduates will have the theoretical and practical competence required to work across a variety of industries within numerous types of organisations. Graduates will also be qualified to continue developing their expertise through further study.

Year 1:

  • Problem Based Learning and Research Methodologies
  • Introduction to Information Security
  • Professional Aspects of Computing
  • Introduction to Programming
  • Discrete Mathematics
  • Network Principles
  • Programming and Databases
  • Studio project work

Year 2:

  • Object Oriented Programming
  • Operating File Systems
  • NoSQL Databases
  • Statistial Analysis Tools and Techniquies
  • Professional Software Development
  • Algorithms and Data Structures
  • Studio project work

Year 3:

  • Final Year Project
  • Big Data Analytics
  • Data Visualisation
  • Machine Learning
  • Elective
  • Elective

Electives:

  • Smart Societies Health, Society and Media
  • Smart Technologies: Computing, Telecommunications and Cyber Security
  • Smart Industries: Oil, Gas and Engineering
  • Natural Language Processing
  • Cryptography and Steganography
  • Incident Management
  • Further Discrete Mathematics
  • Pure Mathematics for Computing

Knowledge:

  • Has broad knowledge of the important topics, theories, principles and issues in data science, big data analytics and related fields, and the associated theoretical and digital processes, tools and methods for investigating data-driven problematic situations.
  • Is familiar with current research and development work in the domain of big data analytics and data science.
  • Has knowledge of the key software development and data analysis principles, theories, tools and techniques for working with large heterogeneous data sets, how to apply them across a variety of data-driven domains and situations, and how to evaluate their efficacy and the results obtained from their application.
  • Can update his/her knowledge in the area of data science through academic study, research and professional development.
  • Has knowledge of the history and development of big data analytics and data science, including the principal tools, techniques and technologies in the data science domain, and their past and potential future impact on the function, management, analysis and development of science, industry and society.
  • Understands the legal and ethical issues relating to obtaining and analysing big data, and presenting the results of big data analysis to stakeholders.
  • Has knowledge of applying data science principles, and statistical and analytical tools and techniques, within complex scientific, societal and industrial fields.

Skills:

  • Can apply academic and theoretical knowledge of data analytics tools and techniques, plus current research and development work, to practical and theoretical data science problems, in order to make well-founded, informed and justified decisions and choices.
  • Can reflect upon own academic practice and professional development, identify areas for improvement, and adapt to future developments in data analytic and visualisation tools, techniques and technology.
  • Is able to find, evaluate and refer to relevant information and scholarly subject matter and present it in a manner that sheds light on data-driven problems.
  • Can appropriately and effectively locate, procure, manipulate and analyse large heterogeneous data sets using appropriate data analytics technologies and statistical techniques.
  • Is able to extract meaning from and interpret data, using a variety of mathematical and machine learning tools and methods.
  • Can select and use the primary digital tools and techniques for visualising data and the results of big data analytics in an appropriate and professional manner, in order to develop and present informative insights into data-driven problematic situations.
  • Can critically select and apply a range of analytical and methodological problem solving techniques, based on research, and to be able to interpret the solutions and present results appropriately.
  • Is able to identify stakeholders of data science projects and communicate, network and collaborate with these stakeholders appropriately according to project requirements and the potential impacts of results.

General Competence:

  • Is able to identify and appropriately act on complex ethical issues arising within academic and professional practice as a Data Scientist.
  • Is able to plan, execute and manage a variety of assignments and data science-related projects over time, alone or as part of a group, to successful conclusion and in accordance with relevant ethical requirements and principles.
  • Can communicate the results of theoretical, practical and research-based academic work effectively using appropriate forms of communication (electronically, orally and/or written) in order to present theories, arguments, problems and solutions in an appropriate, professional manner.
  • Can communicate and exchange opinions, ideas and other subject matters such as theories, problems and solutions, with others with background and/or experience in data science and related fields, through the selection and application of appropriate methods of communication, thereby contributing to the development of good practice within the data science community of practice.
  • Is able to engage in self-reflection as part of the lifelong learning strategy required of a data science  professional and a reflective practitioner.
  • Is familiar with current and new thinking and trends within the field of data science and related disciplines.

Career Opportunities

The expertise and competence this bachelor degree provides is highly sought after in the job market. There are strong indicators that data science, machine learning, artificial intelligence, and Big Data-related challenges will increase in all sectors.

New smart technology, the Internet of Things and Cyber ​​Physical Systems will generate huge amounts of data that require data science specialists. There is a growing need for expertise, and it is urgent for many.

Most large companies that rely on information technology also need people with expertise in data science. This bachelor's degree provides a unique qualification for dealing with challenges in a variety of organizations and the industrial sector.

Further Studies

Students who wish for further training in Data Science can apply for Masters level studies related to computing, data analytics or data science at a variety of higher education institutions.

Apply now

Course information

Next startup:

August 16, 2021.
Read more about semester start.

Campus: Kristiansand and Online Studies
Duration: 3 years
Program
language:
English
Price online:

EUR 4.400,- per semester.
EUR 190,- admission fee.

Price on-campus:

EUR 5.900,- per semester.
EUR 190,- admission fee.

Admission requirements: Math R1 (2MX), or S1 and S2*
Three-year upper secondary education or a vocational education certificate. You may also apply for admission by prior learning. Read more.
Approvals: Approved for loans and grants from the State Educational Loan Fund. Accredited by NOKUT.
Degree: Bachelor
Credits: 180 ECTS

*Math R1, or S1 and S2 is required for admission to the Bachelor of Applied Data Science. (Not required for mature students - over 25 - with relevant work experience.)

Lecturing staff

Prof. Iain Sutherland

Dean of Faculty/Professor
Dr. Isah A. Lawal

Dr. Isah A. Lawal

Associate Professor
Dr. Rayne Reid

Dr. Rayne Reid

Associate professor
Dr. Francois Mouton

Dr. Francois Mouton

Associate professor

Dr. Mikhaila Burgess

Associate professor
Dr. Angesh Anupam

Dr. Angesh Anupam

Associate professor

Prof. Fabricio Bortoluzzi

Assistant professor
Piet Delport

Piet Delport

Assistant Professor
Ruan Koen

Ruan Koen

Assistant Professor
Emlyn Butterfield

Emlyn Butterfield

Associate Professor
Veronica Schmitt

Veronica Schmitt

Associate Professor
Tom Drange

Tom Drange

Lecturer
Konstantin Lenchik

Konstantin Lenchik

Tutor and teacher
Mariya Chirchenkova

Mariya Chirchenkova

Tutor and teacher

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