IT education that focuses on skills and techniques for extracting actionable knowledge from data. The Applied Data Science program is a multidisciplinary one that incorporates the knowledge of Computer Science, Statistics, Data Analysis, Data Visualization, Machine Learning, and Artificial Intelligence.
Highly sought after on the labour market
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 21st century, and is to the digital economy what oil is to 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 is driven by their ability to extract insight from data and to create data applications. Thus, the rise in these data applications and the availability of numerous, diverse specialized data sets means that computing professionals with a degree in Applied Data Science will be needed to work across all subject domains, including science, industry, and government, to extract value and insight from data.
This bachelor program teaches you scientific methods for working with data in a practical and relevant way. 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.
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. The first year serves as a common foundation with Digital Forensics and Cyber Security. Students across all three programs collaborate on a year-long project, which helps cement and enrich the material covered.
The second year of the degree is more in-depth and specialized. Students will further develop their programming and software development skills by learning data structure and algorithms, object-oriented programming and professional software development. 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. Alongside these courses, students will also undertake a final project through the exploration of relevant data and applications.
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.
- 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
- Object Oriented Programming
- Operating File Systems
- NoSQL Databases
- Statistial Analysis Tools and Techniquies
- Professional Software Development
- Algorithms and Data Structures
- Studio project work
- Final Year Project
- Big Data Analytics
- Data Visualisation
- Machine Learning
- 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
- 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.
- 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.
- CPU: Intel i5 (5th Generation or later - or AMD equivalent)
- Apple M1 CPU is not suitable as the required software will not run on it.
- RAM: 16GB
- Storage: 500GB (1TB recommended)
- USB Drive: 8-16GB (2-3 suggested)
- Operating System:
- VMware Workstation is used for teaching and is available once registered at Noroff.
- Microsoft Windows 10 with an option to dual boot or launch alternative operating systems in a Virtual Machine.
- Many students find the use of Windows is a good starting point to allow them to develop the skills to configure alternative operating systems.
- Tutorials will require access to a Windows operating system (either as a host or as a virtual machine)
- Full administrative privileges to install and manipulate all aspects of the system.
- Monitors: Dual monitors or a single wide screen with equivalent display size are recommended for ease of use for working concurrently with documents and applications.
- Web camera & Microphone
- Students are expected to interact with staff and peers via video and voice calls.
- A good quality headset (headphones + microphone) is recommended.
Students studying ADS, require a separate GPU for second and third year. This must support the Turing Architecture (Nvidia 10 and 16 Series cards) or later. Specifications will be confirmed at the end of your first year.
Additional RAM and hard disk storage (1TB) and/or an upgrade to a SSD/NVMe drive are recommended for better performance.
Apple Mac systems are not suitable for Applied Data Science due to possible software incompatibilities.
The expertise and skills from the Data Science bachelor program are sought after as many trend indicators suggest data applications will be 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 will continue to generate a vast amount of data requiring data science specialists.
There are many career opportunities for a graduate with a degree in Data Science. For example, a graduate can be employed as a Data Scientist, Data Engineer or Machine Learning Engineer. A Data Scientist is someone who looks at a company or industry data and use it to answer business questions, make future predictions, then communicate those answers and predictions to other teams in the company to be acted upon. A Data Engineer, on the other hand, manages a company’s data infrastructure. This role requires a lot less statistical analysis and a lot more software development and programming skills.
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.
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.