Bachelor in Applied Data Science

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Bachelor in Applied Data Science

Study Facts
- Area of Study
  Computing
- ECTS
  180
- NQF Level
  Bachelor's degree (Level 6 1. Cycle)
- Campus
  Kristiansand, OnlinePLUS - Bergen, OnlinePLUS - Oslo, Online
- Study Mode
  OnlinePLUS, Full-time, Online
- Entry Requirements
  Entry Requirements
- Study Programme Leader
  Isah Lawal

Introduction

Noroff University College (NUC) offers awards that specialise in the utilisation of digital technology. The objective of the Bachelor in Applied Data Science is to provide you with an understanding of the whole life cycle of the data science process, from data acquisition and exploration to analysis and communication of the results. There is an increasing level of commercial interest in this program area as industry seeks to gain the maximum advantage from the intelligent analysis of large unstructured data sets that will allow them to transform data from raw material to product. Thus, Data Scientists are needed across a wide variety of employment sectors including medicine, manufacturing, natural sciences, pharmaceuticals, finance, retail, and engineering. Students will have an opportunity to develop domain expertise in the final year of the degree. Looking at the requirements of the industry sectors of Oil and Gas, Energy, Engineering and Information Technology, and society-related sectors of government and healthcare.

The aim is to help you develop the required skills to extract actionable insight from data and to create data-driven solutions to real-world problems. This information can be used by stakeholders to facilitate their decision-making process and this will encompass the collection of appropriate datasets for a given problem, development of database for data storage and processing, statistical analysis of the data, predictive analytics and data visualization and communication. The degree programme will challenge you to develop a scientific, rigorous approach to your work. This will enable you to not only solve issues posed as part of the course but also to address unforeseen problems once employed as a data scientist or analyst.

After receiving a general background in applied computing, students will develop skills in database development, statistical analysis, machine learning and software development. Throughout the program students build upon this skill set with their own self-motivated research projects, resulting in graduates that are ready for either employment or postgraduate study.

Students also develop a high level of competency in a variety of specific tools and techniques, along with a solid foundation of knowledge, skills, and competence to support them in lifelong learning throughout their careers. They will have several key attributes:

A deep understanding and practical application of data analytics skill set
Hands-on experience with tools used for data analysis
A high degree of problem-solving skills
A high legal and ethical standard required for data handling

Aims of the Degree Programme

This degree programme aims to develop individuals with the capacity to analyse and understand large data sets and engage in data-driven research and development. Students will have blend of skills and practical experience to become employment-ready graduates with a holistic understanding of both theory and practice in relation to the collection, processing, and analysis of large unstructured data sets.

The core topics addressed in the degree are:

Big data analytics (Data analysis of fast-growing, massive, heterogeneous, and complex datasets).
Software development (appropriate methodologies and programming languages).
Data storage and database technologies (e.g., NoSQL).
Mathematics (mathematical and statistical modelling and analysis).
Big data visualisation (appropriate visualisation concepts, software, tools and techniques).
Artificial intelligence and Machine Learning (theories, technologies, and languages).

Graduates will be able to understand and apply an array of tools and techniques to collect and process very large amounts of data, with the purpose of unlocking and extracting previously unknown knowledge. These skills are required in a wide variety of sectors, including medicine, manufacturing, natural sciences, pharmaceuticals, finance, retail, and engineering. The subject material will enable graduates to go on to postgraduate study in the area and will also enable them to fulfil several distinct employment titles.

Course Model

This bachelor's degree is a full-time programme of study, based on a 40-hour per week study schedule. The degree consists of six semesters which are delivered over three academic years (each of a 10-month duration), with each year providing 60 ECTS of educational material and qualification. This results in a 30 ECTS workload per semester and total of 180 ECTS for the full bachelor’s degree, which equates to 1,500-1,800 hours of work. Table 1 shows how the distribution of ECTS across the programme.

The complete set of courses included is shown in the Course Model(s). The first year is a shared curriculum across all Computing degree programmes. Each year the programme and their courses are reviewed to ensure the degree continues to be appropriate for this fast-developing field. Some courses may span semesters and the credit value is only awarded in the semester they are completed. The courses that comprise this programme are therefore subject to minor changes each academic year. A description of the courses can be found by clicking the course name in the Course Model.

Computing Foundation H24

This is a full-time course model, based on a 40-hour per week study schedule. The foundation consists of two semesters providing 60 ECTS of educational material and qualification. This results in a 30 ECTS workload per semester and is calculated towards a total of 180 ECTS for the full bachelor’s degree in:

Applied Data Science
Cyber Security
Digital Forensics

Course	Course type	2024 Autumn	2025 Spring
UC1PBL05 Problem Based Learning and Research Methodologies	Shared	5
UC1DMA10 Discrete Mathematics	Shared	10
UC1PR110 Introduction to Programming	Shared	10
UC1PR210 Programming and Databases	Shared		10
UC1NPR10 Network Principles	Shared		10
UC1IIS05 Introduction to Information Security	Shared		5
UC1ST110 Studio 1	Shared	0	10
Sum (60 total)		25	35

BAPDS - Applied Data Science H24

Course	Course type	2025 Autumn	2026 Spring	2026 Autumn	2027 Spring
UC2IOS05 Introduction to Operating Systems	Core	5
UC2NDB10 NoSQL Databases	Core	10
UC2PSD05 Professional Software Development	Core	5
UC2OOP10 Object Oriented Programming	Core	10
UC2ADS10 Algorithms and Data Structures	Core		10
UC2SAT10 Statistical Analysis Tools and Techniques	Core		10
UC2ST210 Studio 2	Shared		10
UC3MAL10 Machine Learning	Core			10
UC3BDA10 Big Data Analytics	Core			10
UC3FDM05 Further Discrete Mathematics	Shared				5
UC3PMC05 Pure Mathematics for Computing	Shared				5
UC3DVS10 Data Visualisation	Core				10
UC3BAD20 Bachelor Project	Spesialism			0	20
Sum (120 total)		30	30	20	40

Programme Learning Outcomes

A Programme Learning Outcome (PLO) is essentially a statement that describes what the student has achieved upon successfully completing the degree. Each course description has its own set of learning outcomes, which contribute to the achievement of Programme Learning Outcomes. The PLOs for this degree are based on the Norwegian Qualifications Framework for Lifelong Learning (NQF) at bachelor level. The NQF levels are formulated on the basis of what a person know, can do and is capable of doing as a result of a learning process. The outcomes of the completed learning process are described in the categories: “knowledge”, “skills” and “general competences”.

Knowledge: Understanding of theories, facts, principles, procedures in subject areas and/or occupations.
Skills: Ability to utilise knowledge to solve problems or tasks (cognitive, practical, creative and communication skills).
General Competence: Ability to utilise knowledge and skills in an independent manner in different situations.

Students who are awarded a Bachelor in Applied Data Science have attained:

Knowledge

The candidate ...

K1	has broad knowledge of the theories, principles and issues in data science, big data analytics, and the associated theoretical and digital processes, tools and methods for investigating data-driven problematic situations.
K2	is familiar with current research and development work in the domain of data science.
K3	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 them.
K4	can update his/her knowledge in the area of data science through academic study, research and professional development.
K5	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.
K6	understands the legal and ethical issues relating to obtaining and analysing big data and presenting the results of big data analysis to stakeholders.
K7	has knowledge of applying data science principles, and statistical and analytical tools and techniques, within complex scientific, societal and industrial fields.

Skills

The candidate ...

S1	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.
S2	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.
S3	can find, evaluate and refer to relevant information and scholarly subject matter and present it in a manner that sheds light on data-driven problems.
S4	can appropriately and effectively locate, procure, manipulate and analyse large heterogeneous data sets using appropriate data analytics technologies and statistical techniques.
S5	can extract meaning from and interpret data, using a variety of mathematical and machine learning tools and methods.
S6	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.
S7	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.
S8	can 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

The candidate ...

G1	can identify and appropriately act on complex ethical issues arising within academic and professional practice as a Data Scientist.
G2	can 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.
G3	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.
G4	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.
G5	can engage in self-reflection as part of the lifelong learning strategy required of a data science professional and a reflective practitioner.
G6	is familiar with current and new trends within the field of data science.

Teaching and Learning

Learning Activities

All study programmes use a variety of teaching and learning activities to encourage students to actively explore and apply new knowledge, along with developing skills and competencies. Each course will incorporate a range of teaching and learning methods according to which are most appropriate for that course – determined through a process of constructive alignment. The primary aim of these methods is to support the students’ learning process and facilitate the achievement of the learning outcomes. The applicable teaching and learning methods include, but are not limited to, the following:

Teacher-Led Activities (TLA)
Lectures and video lectures Online lessons Tutorials	Seminars Presentations

Teacher-Supported Work (TSW)
Practical Work Workshops	Scheduled guidance Group work

Self-Study (SST)
Research Reflection	Group work (Individual) Virtual Laboratory Work

Learning Resources

Key information for the degree is delivered in lectures, normally in one of the Campus auditoriums and as a live stream. Tutorials and supported study are delivered through laboratory-based sessions. All educational material is accessible through the LMS, which forms part of the Virtual Learning Environment (VLE), illustrated in Figure 1.

The LMS provides a central location for the distribution of all educational content and learning resources related to all courses throughout the program of study:

Study Plan
Course Descriptions
Lecture slides
Recorded lectures
Reading lists

Student Handbook
Tutorial exercises
Supplementary notes
Self-study material

The dates and times for all educational sessions for every course, including lectures and tutorials, can be found in the online timetabling system (TimeEdit).

Study Workload

The student workload has been carefully considered for each course to include an appropriate combination of activities suitable for the subject area.

Information and details about a specific course can be found in the respective Course Description. However, each course comprises a selection of lectures, tutorials, and other appropriate sessions. These are timetabled based on a full-time study schedule of 08:00 to 16:00, Monday to Friday.

At the start of each academic year, a Study Schedule is published and made accessible. It contains the planned start and end dates for all courses in the degree. The schedule also includes dedicated study time to work on projects and extra-curricular sessions, including seminars, workshops, and guest speakers from industry. If the schedule is updated, students are promptly informed.

Reading List

Reading lists for a study programme, and especially for a course within a study programme, is annually revised. In some cases, where the field is rapidly changing, the reading list may be complete closer to course start-up. The reading list will be shared close to semester start, or upon request by contacting the Study Programme Leader.

Assessment

Grading of Assessments

Each course in the programme of study comprises of several graded (summative) assessments, where students can demonstrate their achievements and abilities. Information about assessments for each course is provided via the course pages on the LMS. When assessments are released, students are encouraged to always read through the instructions fully and carefully, to ensure the greatest chance of success. If anything is unclear, please contact the relevant Course Leader as soon as possible.

A course is successfully completed once the student has obtained a passing grade for that course. Every assessment has a specific completion deadline comprising a date and time. Work can be submitted any time up to the stated deadline. Students must be able to clearly demonstrate the extent to which they have met the learning outcomes of that course in order to pass. Students will encounter a variety of assessments, which may be used for formative and summative purposes, to ensure that students meet or exceeded the PLOs.

Specific assessment strategies for each course, and instructions for submitting course work, are detailed in the LMS course pages. Please see the regulations available on www.noroff.no/en.

Assessment Methods
Formative	Continuous Assessment Online Test Interactive Online Test
Summative	Presentation of Practical Work Portfolio of Work Projects Online Exams Reports Term Paper Oral Test

Grading Scale

Assessments are graded according to the standard university grading scale, described in the table below.

Grade Letter	Quality Indicator	Definition
A	Excellent	An excellent performance, clearly outstanding. Shows a high degree of independence.
B	Very good	A very good performance, above average. Shows a certain degree of independence.
C	Good	An average performance, satisfactory in most areas.
D	Satisfactory	A performance below average, with significant shortcomings.
E	Sufficient	A performance that meets the minimum criteria, but no more.
F	Fail	A performance that does not meet the minimum criteria.

Entry Requirements

For general admission it is required to document the following criteria as passed:

Higher Education Entrance Qualification, and
Candidates must be able to document proficiency in the English language. Language requirements by Samordna Opptak

Special admission requirements
In addition to the general admission requirements, it is required to document the following:

Mathematics R1 (or S1+S2)

For admission on basis of prior learning and work experience:
Admission based on prior experience requires a written application for evaluation. Applicable candidates must be at least 25 years of age in the year of admission.

For candidates with foreign education the requirements for Higher Education are:

The country must be recognized by NOKUT, specified in the GSU-list.
Candidates must be able to document proficiency in the English language. Language requirements by Samordna Opptak

For further information, please see the admission requirements: https://www.noroff.no/en/admission/admission-requirements

Campus and Online Study

All students follow the same progression according to their education plan, irrespective of whether they study online or on campus. All students study the courses at the same time, with the same delivery and workload, following identical assessment strategies for every course. At the study level no distinction is therefore made between campus and online students. All students are required to engage in live education sessions (such as lectures) and undertake all required educational activities.

Students are encouraged to interact with each other via online forums and chat systems, enabling discussions to take place involving both online and campus students. Each student cohort is therefore a single learning community, concurrently engaging in all educational activities irrespective of actual physical location. Throughout all educational sessions course staff actively encourage participation from campus and online students simultaneously, and do not focus solely on those who are physically present.

This tight integration of campus and online ensures students will be part of a cohesive learning community throughout their study. As a result, this also means that should students personal situations change during their studies, and they must change their mode of study from online to campus (or vice versa) this can be done with little to no disruption to their studies.

Opportunities for Further Studies

Undertaking some period of study at an international educational institution can result in many benefits to those who take part, including:

Language and general competence in the destination country and culture
Development of personal and professional networks in other parts of the world
Personal growth and holistic development.

All students are eligible to apply to undertake a period of study at an international university. All international study opportunities are subject to the application processes and admissions requirements of the international institution, in addition to an evaluation of the suitability of the proposed study exchange within the students’ study at NUC. Full details of international study opportunities and the application process is available to all students within the LMS.

Accredited

May 23rd, 2017

Last revision date:

June 2024