Bachelor in Applied Data Science

Education focusing on extracting actionable knowledge from data. This study programme prepares you for an exciting career working with companies to understand and use the vast amounts of data at their fingertips. Additionally, you will develop skills in the creation of intelligent AI systems for businesses using machine learning techniques.

The sexiest profession of the 21st century

We are living in the data age where data are generated from everywhere - posts to social media sites, online sales transactions, sensors, GPS enabled devices, health care and the Internet of Things. Data is a very useful resource of the modern society - the digital economy is what oil is for the industrial economy. The enormous need for – and lack of expertise – means that data scientists are often very well paid.

The success of many tech companies such as Netflix, Spotify, Facebook, Google, and Amazon are driven by their ability to extract insight from data and to create data applications. Data must work across disciplines and domains, including science, industry, and government, to extract value and insights from the data, and support the automation of business processes using intelligent systems built from that data. The rise in data applications and the availability of numerous, advanced data sets means that specialized competence in Applied Data Science is sought after and needed.

We have skilled teachers who are committed to conveying their knowledge. Dior Anvarov, student
The probability of being unemployed after finishing is very small. Babette Weldehanna, student

Embark on a transformative three-year journey: The three-year study programme delves into the art of transforming raw data into something usable. You will gain hands-on experience in the practical application of tools and techniques encompassing, data management, analytics and visualisation, software development and deployment, mathematical and statistical analysis, and artificial intelligence and machine learning.

Take the first step towards an exciting career in data science by applying to the Bachelor in Applied Data Science today! Unlock your potential and become a force in the use of data and information. 

Programme Structure

Applied Data Science consists of 180 ECTS, combining general education and computer science fundamental courses, core data science subjects, and elective courses. The curriculum covers a wide range of topics, including data structures, analytics, visualisation, machine learning, programming and databases.

In your first year you will build a solid foundation in core technical fundamentals, setting the stage for success in the specialized courses further into the education. From the very beginning you will develop essential research and study skills, work as part of a vibrant student community, and collaborate on a project-based course to enrich your learning experience and forge lasting connections with your peers. 

The second year is more in-depth and specialized. You will further develop your competence in programming and software development by digging into data structures, relevant algorithms, object-oriented programming and professional software development. You will explore larger data chunks and statistics using effective tools and techniques for data analysis and data storage technologies.

Your third year culminates in a Bachelor Project, allowing you to apply your newfound competence to innovative systems and applications. Under the expert guidance of our experienced staff, explore the data requirements of the industry sectors of Oil and Gas, Engineering and Information Technology, or society-related sectors of Government and Healthcare. Choose electives to gain additional expertise in advanced areas, preparing you to tackle the challenges of today and tomorrow. 

Become a data expert by joining the bachelor’s degree. Set yourself on the path of both the theoretical and the practical know-how required by modern professionals. Enable yourself to work across a variety of industries within numerous sectors and organisations.  

Year 1: 60 ECTS

  • Problem Based Learning and Research Methodologies (5 ECTS)
  • Introduction to Programming (10 ECTS)
  • Discrete Mathematics (10 ECTS)
  • Programming and Databases (10 ECTS)
  • Network Principles (10 ECTS)
  • Introduction to Information Security (5 ECTS)
  • Studio 1 (10 ECTS)

Year 2: 60 ECTS

  • Introduction to Operating Systems (5 ECTS)
  • NoSQL Databases (10 ECTS)
  • Professional Software Development (5 ECTS)
  • Object Oriented Programming (10 ECTS)
  • Algorithm and Data Structure (10 ECTS)
  • Statistical Analysis (10 ECTS)
  • Studio 2 (10 ECTS)

Year 3: 60 ECTS

  • Machine Learning (10 ECTS)
  • Big Data Analytics (10 ECTS)
  • Data Visualization (10 ECTS)
  • Elective Courses (2 x 5 ECTS)
  • Bachelor Project (20 ECTS)


  • 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.


  • 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.

As an online student you are required to purchase the required equipment and software. As a Campus student the required equipment and software is available for use.

Required Equipment

  • A reliable internet connection
  • Headphones or headset – with a microphone.
  • Webcam.
  • 2 USB Drives with 8-16 GB storage.
  • PC/Laptop with the following specifications:
    • Microsoft® Windows® 10 or 11.
    • CPU: Intel i5 (64-bit Intel® or AMD® multi-core processor is recommended)
    • GPU: Nvidia GeForce GTX (GeForce RTX 3070 is recommended)
    • RAM: 16 GB (32 GB recommended)
    • Storage: 500 GB (SSD/NVMe is highly recommended)

You must have full administrative privileges to install and manipulate all aspects of your computer. The minimum requirements apply to all who purchase/use their own machine. This may not be an Apple Mac, as these systems are not compatible with the required software.

Recommended Equipment

  • 1-2 additional USB Drive with 1-4 GB storage
  • 2 monitors, or 1 ultrawide screen
  • 1 additional storage drive (Minimum 1 TB is recommended)

Required Software

As a student you will be provided student licences, until then you should explore the software.

Many students find that the use of Windows is a good starting point to allow them to develop the skills to configure alternative operating systems. Several tutorials will require access to a Windows operating system (either as a host or as a virtual machine).

Career Opportunities

The expertise and competence gained from this study programme is highly sought after as current trends suggests that data applications will continue to grow, and their data streams are of ever-increasing significance to many industries. The emerging initiatives related to new technologies used in Smart Cities, Internet of Things and Cyber-Physical Systems continues to generate big data, increasing the need for skilled data science specialists.

Most large companies and corporations rely heavily on modern information technology, they need people with expertise in data science to tackle the vast amounts of data, and to deal with the challenges that individual businesses face.

Graduates of Applied Data Science are well-equipped to pursue rewarding careers in various sectors, including:

  • Data science and corporate needs
  • Data engineering and infrastructure
  • Machine Learning and engineering units
  • Data science researchers and academia

Are you ready to become a skilled professional? Apply now for Bachelor in Applied Data Science and embark on an exciting and fulfilling career path. Contact the admissions team for more information on the application process, financial aid, and programme requirements. 

You may also choose to take CCNA certification through Noroff Accelerate. Our 10-week courses are flexible and effective, designed to fit your everyday life. Cisco's CCNA certifications are among the most recognized in the IT industry and provide a solid foundation for further careers. 

Further Studies

Students who wish for further training in data science can apply for studies related to computing, data analytics and data management at the master level by Norwegian or international universities. 

Watch recording of livestream

We recommend watching the latest livestream from the Computing bachelor’s degrees hosted by Emlyn Butterfield and recorded 10 March 2023. You can also skip directly to the chapter regarding Applied Data Science in the chapter menu, which starts at 03:17.

Course information

Next startup:

August 12, 2024.
Read more about semester start.

Locations: Kristiansand,
Online and
Online PLUS Oslo
Duration: 3 years
Price Online:

EUR 4.900,- per semester

Price Online PLUS Oslo:

EUR 5.700,- per semester

Price Campus Kristiansand:

EUR 6.100,- per semester.
EUR 190,- admission fee.

Admission requirements: Three-year upper secondary education. You may also apply for admission by prior learning.
In addition there is a special requirement of Math R1, or S1 and S2, or international equivalent. Other documented knowledge or education can be accepted as equivalent. Read more.
Approvals: Approved for loans and grants from the State Educational Loan Fund. Accredited by NOKUT.
Degree: Bachelor
Credits: 180 ECTS


Prof. Iain Sutherland

Professor of Digital Forensics
Professor Johan van Niekerk

Prof. Johan van Niekerk

Professor of Cyber Security
Prof. Barry Irwin

Prof. Barry Irwin

Professor of Cyber Security
Prof. Seifedine Kadry

Prof. Seifedine Kadry

Professor of Applied Data Science
Dr. Isah A. Lawal

Dr. Isah A. Lawal

Study Programme Leader
Dr. Rayne Reid

Dr. Rayne Reid

Associate professor

Fabricio Bortoluzzi

Associate professor
Piet Delport

Piet Delport

Associate professor
Emlyn Butterfield

Emlyn Butterfield

Head of Computing
Veronica Schmitt

Veronica Schmitt

Assistant professor
Mariya Chirchenkova

Mariya Chirchenkova

Assistant professor

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