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.
The Data Explosion
We are living in the data age! Data comes from everywhere – posts to social media sites, online sales transactions, climate and traffic sensors, GPS enabled devices, cell phone systems, transportation networks, industry systems, healthcare and the Internet of Things. Data is being generated at a constantly accelerating rate by both humans and machines. IBM estimates that every day 2.5 quintillion bytes of data is generated, with 90% of existing data having been created in the last two years alone.
The rise of Big Data and the availability of numerous, diverse specialised data sets means data experts are needed to work across all subject domains, including science, industry and government, working across the whole data life-cycle, from acquisition, cleansing and exploration to analysis, visualisation and communication. This is the domain of the Data Scientist.
Data are to this century what oil was to the last one: a driver of growth and change.The Economist
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.
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.
- 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.
The expertise and skills of this bachelor program are sought after as many trend indicators suggest Data Science and ‘Big Data’ related problems will be of ever increasing significance to many commercial sectors. This has been driven over recent years by developments in technology and the ubiquity of data. The emerging initiatives related to new technologies used in Smart Cities, Internet of Things and Cyber Physical Systems will also generate a vast amount of data requiring data science specialists. There is an urgent need for graduates skilled in large-scale data analysis.
According to Abelia there is a worrying deficit of people with strong technology skills in Norway. The distance between needs and available expertise ranges from 24 to 113 percent. The best-case scenario suggests that by 2030, one in four ICT positions will be vacant.
McKinsey estimates that the USA has a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data. This is estimated as a 50-60% gap in the demand for analytical experts. A report by the Royal Statistical Society in the UK has highlighted that 80% of organisations are already having issues finding the skill set to fill the increasing demand.
Most large businesses that rely on information technology have the need for people with expertise in Data Science. This bachelor's degree therefore provides a unique qualification for handling challenges across a variety of organisations and industry sectors.
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 both within Norway an internationally. Graduates who wish to pursue doctoral level studies would then be able to apply for such study opportunities in Norway or beyond.