BSc (Hons) Computer Science (Applied Data Science) (Online)

Key information

Typical offer:
Entry requirements
Fees: See below
Full details
Institute code: H36

The University of Hertfordshire is committed to welcoming students with a wide range of qualifications and levels of experience. The entry requirements listed on the course pages provide a guide to the minimum level of qualifications needed to study each course. However, we have a flexible approach to admissions and each application will be considered on an individual basis.

UCAS points	A level	BTEC	Accepted T Levels	Access Course Tariff	IB requirement
120–128	BBB–ABB	DDM	120–128 UCAS points Please see additional requirements for accepted T Level subjects	Overall merit profile in 45 credits at Level 3	120–128 UCAS points from a minimum of two HL subjects at grade 4 or above

Additional requirements

GSCE: Grade 4/C in English Language and Mathematics

Accepted T Levels: Design Survey and Planning for Construction; Design Production, Design and Development; Education and Childcare; Healthcare Science; Health; Science; Digital Support Services; Digital Business Services; Onsite Construction; Building Services Engineering for Construction; Accounting; Design and Development for Engineering and Manufacturing; Engineering, Manufacturing, Processing and Control; Finance; Maintenance, Installation and Repair for Engineering and Manufacturing; Management and Administration; Agriculture, Land Management and Production; Legal Services.

All students from non-majority English speaking countries require proof of English language proficiency, equivalent to an overall IELTS score of 6.0 with a minimum of 5.5 in each band.

If you do not have the required IELTS or equivalent for direct entry on to your degree programme, our Pre-sessional English and International Foundation courses can help you to achieve this level.

Find out more about international entry requirements.

Why choose this course?

Study where and when you want, fitting in with other commitments such as work or family
Our online degrees are based on their on-campus equivalents with the same academic quality standards
Study at a well established computer science department with strong links to business

You will have full flexibility in your studies: This course is taught solely online, with most presentations recorded, giving you the convenience to study around your other commitments.

Study at a well-established computer science department, with strong links to business: You’ll benefit from our large team of experienced academics and educational technologies, to ensure you have a great learning experience.

You'll be able to take part in CV-boosting co-curricular activities: Workshops introducing programming languages such as JavaScript and Python, and client-side web technologies including HTML5 and CSS. With the AWS Academy Cloud Foundations, you'll receive a course completion certificate, and will also have the opportunity to go for the AWS Cloud Practitioner certification which is an industry recognised accreditation.

Excellent employment prospects: Computer science graduates are in high demand. 93% of Herts graduates are in work or further study 15 months after graduating (Graduate Outcomes 2021/22).

What's the course about?

This data science online degree is based on our BSc Computer Science course that runs on campus, with the same academic standards, processes and worldwide award recognition. As the course is delivered online, you do not need to attend timetabled sessions: the course is designed to fit around your schedule, giving you flexibility in when and where you study.

Our BSc Computer Science (Applied Data Science) (Online) is designed for students looking to build a solid foundation in applied data science, preparing you for an impactful career. You’ll develop critical problem-solving and analytical skills to meet the demands of an evolving digital landscape. Graduates are well equipped for roles in a range of sectors, benefiting from our connections with industry leaders and real-world insights.

What will I study?

This course is taught 100% online, giving you flexibility to study when and where you want.

You will be provided with a suite of learning materials, supporting you to produce your best work: You’ll have access to a comprehensive digital library, including research papers and e-books, as well as a Virtual Learning Environment (VLE) with features to support your learning such as discussions forums, quizzes and practice assignments. This is also a central place where you can find the learning materials for your modules and interact with tutors and other students on the course.

Most modules have a minimum of four hours of online sessions, and presentations are usually recorded.

You will have the support of your tutors via online discussions or optional live sessions. You will also be part of our online learning community, and our students tend to use this to support each other.

In the first year, you will learn about the foundations of Computer Science, including a problem-solving approach to programming, modelling and representing data, computer design, networks, operating systems and devices.

In the second year, you will deepen your understanding and develop more advanced skills. You’ll learn about accessibility and usability issues and study in a team to complete a data science project. Modules also cover artificial intelligence, software development, databases, algorithms and data structures, operating systems and networks.

In the third year, you will study cybersecurity and contemporary issues in the social, professional, ethical and legal aspects of computing. You'll also start specialising in Applied Data Science, learning about data mining and visualisation, principles of data science, machine learning and neural networks. Finally, you will complete an individual project: this is your opportunity to apply what you've learnt on a topic of interest to you. You'll be guided by an academic supervisor throughout the project.

Level 4

Module	Credits	Compulsory/optional
Computational Problem Solving (COM)	30 Credits	Compulsory
This module makes use of discrete mathematics and logic both as sources of example problems and as tools for describing and reasoning about programs. In the programming strand the emphasis is on designing and implementing solutions to well-specified problems. Students will be given guidance on problem representation and choice of data structure and expected to choose / write appropriate algorithms for themselves, and to implement them in Python. In the discrete structures strand the emphasis is on understanding how mathematical and logical structures and reasoning may be used in the service of programmatic problem-solving. This strand will also serve as a source of example problems for students to solve.
Data Modelling for Databases (COM)	15 Credits	Compulsory
This module introduces the concept of the relational model of database systems introduced by Codd. It will focus on the principles of data modelling, from conceptual modelling through to physical, it will introduce techniques and tools to possibly include but not be limited to Requirements Gathering, Whiteboarding, Entity-Relationship Modelling, Normalisation, Relational Algebra, Physical Design and Querying via SQL. The module also introduces some of the basic operational requirements of a modern enterprise RDBMS with topics supporting the students understanding of the physical design of the database, hence subjects such as Query Optimisation and Transaction Processing will be introduced.
From Silicon to C (COM)	30 Credits	Compulsory
This module focuses on the principles and practicalities of the multilevel organisation of computer systems. Students will have opportunities to investigate and build computational structures in hardware, and to program with machine instructions and a simplified dialect of the C programming language and see how they support one another. The abstractions available at higher levels (programming languages) w ill be de-mystified by progressively bringing them down to lower levels all the way to the silicon structures that serve as the foundation of any computer system. This will help students to learn high-level programming (w hen they do) in an informed and efficient way by creating cost intuitions that cannot be developed while keeping solely to abstract constructs such programming normally deals with. Students will be offered a significant number of development exercises ranging from very basic to challenging and will be helped. Independent learning by practicing is key to this.
Introduction to Programming and Discrete Structures (COM)	30 Credits	Compulsory
This module will enable students to think programmatically about problems that are solved on a computer and help them develop problem-solving skills. There are two strands to the module. The programming strand focuses largely on the practicalities of programming. A programming language, Python, will be introduced, paying much attention to the fundamental elements of programming: algorithmic and functional decomposition, writing assignments, conditionals, loops and functions, and techniques for testing and debugging. This will be illustrated with a progression of examples to help students develop good comprehension of code and the coding process. The discrete structures strand focuses on understanding and reasoning about mathematical and logical structures that are of relevance to programmers. It will introduce students to a coherent set of concepts from discrete mathematics and logic, including sets, relations, functions, propositions, predicates, and automata, and will illustrate how they may be applied in the modelling and solving of computational problems.
Team Software Project (COM)	15 Credits	Compulsory
This module is designed to develop a range of employability skills using a guided project, undertaken as a team, as the vehicle. Each student will work on the project within their team. Project specifications will be provided that ensure the students have an opportunity to develop project and time management, professional, team communication and reporting, risk, presentation, and self-evaluation skills. The projects are to be based on typical commercial/industrial scenarios and incorporate the concepts of specification, design, implementation, and testing. Where possible, the guided projects will be constructed with reference to the United Nations Sustainable Development Goals.

Level 5

Module	Credits	Compulsory/optional
Programming, Algorithms & Data Structures (COM)	30 Credits	Compulsory
This module develops students' programming skills by focusing on the study and development of algorithms and data structures appropriate for a range of different types of problem. The module emphasises throughout the role of problems as an essential driver in the programming process and the design of algorithms and data structure in particular. Throughout, implementations will be developed within a relevant programming language, good practices promoted for software development, and links drawn between the studied algorithms/data structures and those provided in the language's standard library.
Accessibility and Usability (COM)	15 Credits	Compulsory
This module is concerned with the accessibility and usability of computing applications. It presents these core concepts in the broader context of legal, social, ethical and professional issues in computing and grounds them on practical work. Concepts and theories within these themes will be presented and supported by examples to demonstrate how they occur in practical scenarios. Students will be encouraged to identify existing problems, evaluate potential solutions, and communicate these in computing contexts.
Database Systems (COM)	15 Credits	Compulsory
This module assumes an understanding of fundamental relational database design, implementation, and use. It will introduce other database paradigms including, but not limited to Business Intelligence and Data Warehousing, "Schema-free"/NoSQL databases and Graph Databases. Teaching will be directed at both the practical design, implementation and use of these systems as well as understanding the key advantages of each, providing an ability to compare and critique their appropriateness in a range of scenarios. In using these systems, the module will consider the limitation and appropriateness of query languages and the interaction between query languages and programming languages. The module will also provide insights into the key functional requirements of various database management systems covering topics such as Data Consistency, Transaction Management, Query Optimisation, Security and Scalability/Distribution. This module will involve a large practical component, with a range of tools and case studies being utilised to develop practical skills and theoretical understanding.
Artificial Intelligence (COM)	15 Credits	Compulsory
This module will give a first introduction to the aims, scope, paradigms, notions, methods and algorithms of Artificial Intelligence (AI), enabling students to program basic AI algorithms and systems. Indicative topics include: Logic-based reasoning and representation and knowledge-based systems; Programming for AI: recursion and recursive problem specification; Basic representation- and search-based problem-solving techniques; Introduction to systems that plan and act logically: making plans to achieve goals and making simple decisions; Introduction to AI systems that make decisions in interaction with the world: simple Embodied AI systems; that make decisions based on limited sensorimotor information; Bio-inspired computing methods and algorithms; Modelling embodied intelligence in robots: simple sensorimotor and behaviour-based control architectures for autonomous cognitive robots.
Data Science Development Exercise (COM)	30 Credits	Compulsory
The core of the module is a realistic data science case study, undertaken in small teams, to develop a software system that makes use of a large dataset. Developing such a system is not just a matter of understanding the basics of software engineering and project management, but also requires skills acquired through hands-on practice. This approach of learning through experience involves the challenge of identifying, comprehending and critically handling a number of advanced methodologies, concepts, techniques and technologies.
Operating Systems and Computer Networks (COM)	15 Credits	Compulsory
This module covers both theory and significant practical content in the design, installation and configuration of operating systems and network services. The module helps to develop problem-solving skills in working with operating systems and networks, and builds confidence in using command line tools and configuration files in other areas of computer science and information technology.

Level 6

Module	Credits	Compulsory/optional
Computer Systems Security (COM)	15 Credits	Compulsory
This module provides a description of computer security techniques and mechanisms and the underlying design trade-offs. Furthermore, it discusses risk and threat methods and methodologies. The module covers cryptographic tools, access controls and techniques for equipping students to scope, manage and conduct a penetration test.
Data Mining and Visualisation (COM)	30 Credits	Compulsory
Data Mining is an analytic process for discovering hidden knowledge, unexpected patterns and new rules from large datasets. The major tasks for data mining include association rule mining, classification, cluster analysis and anomaly detection. There are various algorithms for each of these tasks. In general, data mining techniques are used in organisations which collect data. Potential applications include market analysis and management (target marketing, customer relation management, market basket analysis, cross selling, market segmentation), risk analysis and management (forecasting, customer retention, quality control, competitive analysis), fraud detection and management, text mining (news group, email, documents) and web analysis, etc. Data visualisation is about designing ways of presenting and effective interaction with data. Potential applications of data visualisation include market data, media and web data, and scientific data. Data visualisation often works with the results of data mining, to present the knowledge, patterns and rules uncovered from a dataset.
Responsible Computing (COM)	15 Credits	Compulsory
This module is concerned with legal, social, ethical and professional issues that may affect the work of professionals in the computing and technology sectors. The module is designed to enable students to make informed judgements in computing practice based on legal, social and ethical principles.
Machine Learning and Neural Networks (COM)	15 Credits	Compulsory
This module uses contemporary machine learning and neural network techniques to aid in the understanding and analysis of real-world data and neural systems. Topics include supervised and unsupervised learning, data visualisation, and error-correction-based learning.
Principles of Data Science (COM)	15 Credits	Compulsory
This module will include a study of a selection of topics centred around basic mathematical concepts and skills that are relevant to data science solutions. For example: basic linear algebra, calculus, basic statistics, probability, and information theory. Actual topics taught may vary from year to year to reflect developments in the field.
Data ScienceProject (COM)	30 Credits	Compulsory
This module is almost exclusively self-directed study. The actual academic content is defined by the topic chosen by the student. The Project module provides an individually designed programme of study based around the principles of the chosen degree title. This programme of study should reflect a solution to a problem of the student s devising. In line with BCS accreditation requirements, projects must involve practical work leading to the creation of a technical artefact. Projects based purely on literature review or surveys are not permitted.

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What's next for my career?
Applied data science graduates are highly sought after around the world. Our graduates have an excellent employment record, with 93% employed or in further study 15 months after graduation (Graduate Outcomes, 2021/22).

Typical roles for applied data science graduates include:
- software developer or engineer
- businbess analyst
- database administrator
- network designer
- technical consultant
- programmer
- web Developer
- project Manager
We will get you ready to stand out in the graduate job market. This data science online degree is specifically designed to ensure you leave with the skills and knowledge to succeed in your career. You will benefit from CV-boosting opportunities that further enhance your employability.
Key staff

Programme Leader Dr Andrew Pyper

Find out more about Programme Leader Dr Andrew Pyper

Course fact sheets
BSc (Hons) Computer Science (Applied Data Science) (Online)	Download

Programme specifications
Modular BSc (Hons) Computer Science (Online)	Download

Additional information
Sandwich placement or study abroad year	n/a
Applications open to international and EU students	Yes
Course length	Online - Part Time, 6 Years
Location	University of Hertfordshire, Hatfield

How to apply?

2025
Start Date End Date Year Location Link
26/09/2025 31/05/2026 1 UH Hatfield Campus Apply online (Distance (Part Time))
23/01/2026 31/01/2027 1 UH Hatfield Campus Apply online (Distance (Part Time))
2026
Start Date End Date Year Location Link
26/09/2026 31/05/2027 1 UH Hatfield Campus Apply online (Distance (Part Time))
23/01/2027 31/01/2028 1 UH Hatfield Campus Apply online (Distance (Part Time))
Fees and funding
Fees 2024
UK Students
Part time
- £865 per 15 credits for the 2024/2025 academic year
EU Students
Part time
- £865 per 15 credits for the 2024/2025 academic year
International Students
Part time
- £865 per 15 credits for the 2024/2025 academic year
Fees 2025
UK Students
Full time
- £7335 for the 2025/2026 academic year
Part time
- £915 per 15 credits for the 2025/2026 academic year
EU Students
Full time
- £7335 for the 2025/2026 academic year
Part time
- £915 per 15 credits for the 2025/2026 academic year
International Students
Full time
- £7335 for the 2025/2026 academic year
Part time
- £915 per 15 credits for the 2025/2026 academic year
*Tuition fees are charged annually. The fees quoted above are for the specified year(s) only. Fees may be higher in future years, for both new and continuing students. Please see the University’s Fees and Finance Policy (and in particular the section headed “When tuition fees change”), for further information about when and by how much the University may increase its fees for future years.

View detailed information about tuition fees

Read more about additional fees in the course fact sheet

Other financial support

Find out more about other financial support available to UK and EU students

Start Date	End Date	Year	Location	Link
26/09/2025	31/05/2026	1	UH Hatfield Campus	Apply online (Distance (Part Time))
23/01/2026	31/01/2027	1	UH Hatfield Campus	Apply online (Distance (Part Time))

Start Date	End Date	Year	Location	Link
26/09/2026	31/05/2027	1	UH Hatfield Campus	Apply online (Distance (Part Time))
23/01/2027	31/01/2028	1	UH Hatfield Campus	Apply online (Distance (Part Time))

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BSc (Hons) Computer Science (Applied Data Science) (Online)

Key information

Typical offer:

Fees: See below

Institute code: H36

Additional requirements

Why choose this course?

What's the course about?

What will I study?

Check out our student blogs

Student Blogs

2025

2026

Fees 2024

UK Students

Part time

EU Students

Part time

International Students

Part time

Fees 2025

UK Students

Full time

Part time

EU Students

Full time

Part time

International Students

Full time

Part time

Other financial support