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MSc Data Science and Analytics

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Why choose Herts?

Industry Accreditation: BCS accreditation will be sought once this award has its first group of graduates in 2020.
Work Placement Opportunities: You have a one-year industry placement option enabling you to gain real industry experience.
Employment Prospects: Graduates in our department are now data scientists, data engineers, and data analytics consultants working for organisations including BT, IBM, and Microsoft.

For admission to MSc Data Science and Analytics, the normal requirement is a good Honours Degree (or equivalent) in computer science or cognate discipline. The choice of award title students may be accepted on to will be determined by the award applied for and the prior learning of the student as demonstrated by the transcript for existing qualifications held by the applicant.

Applicants whose first language is not English must demonstrate sufficient competence in English to benefit from the Programme. This is normally demonstrated by recognised awards equivalent to an overall IELTS score of 6.0. Candidates who do not satisfy these requirements will be considered on a case-by-case basis.

The programme is subject to the University's Principles, Policies and Regulations for the Admission of Students to Undergraduate and Taught Postgraduate Programmes (in UPR SA03), along with associated procedures. These will take account of University policy and guidelines for assessing accredited prior certificated learning (APCL) and accredited prior experiential learning (APEL).


Institution code	H36
School of study	School of Physics, Engineering and Computer Science
Course length	Full Time, 15 Months Full Time, 1 Years Part Time, 3 Years
Location	University of Hertfordshire, Hatfield

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Employability

What job can I get?

Our master's programme is designed to give Computer Science graduates the specialist, up-to-date skills and knowledge sought after by employers, whether in business, industry, government or research.

The MSc Data Science and Analytics course will equip students with the knowledge and practical skills in data science and analytics from the foundations of data science in mathematics and programming, data mining, machine learning, to data analytics applications.

Successful graduates may pursue a career as a data scientist or data engineer, data analytics consultant, digital marketing manager across a wide range of sectors.

Work placement

This MSc is available with an optional one-year industry placement.

This offers you the opportunity to work for one year in a highly professional and stimulating environment. You will be a full time employee in a company earning a salary and will learn new skills that can't be taught at University. During the placement, you will be able to gain further insight into industrial practice that you can take forward into your individual project.

Professional Accreditations

This new version of the programme has been carefully designed to achieve appropriate levels of accreditation, and BCS accreditation will be sought after the award title has its first group of graduates in 2020.

About the course

The MSc in Data Science and Analytics utilises our strong international reputation for research in these areas. The expertise of our staff influences this course which explores current research and practice in data science and analytics, extending students’ knowledge and understanding of the principles and practice of obtaining data from various sources, the essential methods for pre-processing and cleaning data, data analytics and modelling.

This course is part of our Master's programme in Computer Science.

Why choose this course?

This MSc is available with an optional one-year industry placement.
One of a range of advanced courses within our postgraduate Master's programme in Computer Science, this particular course provides you with a specialism in Data Science and Analytics
Advanced topics studied include mathematics and programming of data science, data mining, machine learning and neural artificis, and data analytics applications.
Taught by a highly-regarded and long-established computer science department.
Computer Science saw 90% of its research ranked as world-leading (Research Excellence Framework, 2021).

What will I study?

Classes consist of lectures, small group seminars, and practical work in our well-equipped laboratories. We use modern, industry-standard software wherever possible. There are specialist facilities for networking and multimedia and a project laboratory especially for masters students. In addition to scheduled classes, you will be expected a significant amount of time in self-study, taking advantage of the extensive and up-to-date facilities. These include the Learning Resource Centres, open 24x7, with 1,500 computer workstations and wifi access, Studynet our versatile online study environment usable on and off campus, and open access to our labs.

Level 7

Module	Credits	Compulsory/optional
Data Mining	15 Credits	Compulsory
Data Mining deals with the discovery of hidden knowledge, unexpected patterns and new rules from large databases. It is currently regarded as the key element of a much more elaborate process called Knowledge Discovery in Databases (KDD), which is closely linked to another important development - data warehousing. The combination of data warehousing, decision support, and data mining indicates an innovative and totally new approach to information management. Until now, information systems have been built and operated mainly to support the operational processes of an organisation. KDD views the information in an organisation in an entirely new way - as a strategic source of opportunity (Adriaans 1996).
Neural Networks and Machine Learning	30 Credits	Compulsory
A study of a selection of research topics centered around neural network theory and design, machine learning including supervised and unsupervised learning and some interesting applications, for example, data mining, biocomputation, evolutionary algorithms, neural networks as models of brain function in health, disease and development, and data visualization. Actual topics taught may vary from year to year.
Foundations of Data Science	30 Credits	Compulsory
- A study of a selection of topics centred around basic mathematical concepts and skills. For example: basic linear algebra, calculus, basic statistics, probability and fundamentals of Bayesian inference, basic set theory and information theory. Actual topics taught may vary from year to year. - Instantiating some of the mathematical foundation as computational techniques.
Applied Data Science and Analytics	15 Credits	Compulsory
This module provides a hands-on experience in all aspects of typical data science tasks. This module focuses on the practical aspects of Data Science, such as data retrieval and pre-processing, data analysis and modelling, and visualisation of the results. We will study typical data science workflows with the help of concrete use cases. Along the way, essential tools for data science are introduced.
Data Science and Analytics Masters Project	60 Credits	Compulsory
The project is a showpiece opportunity for students to demonstrate what they know about current research and practices in data science and analytics and demonstrate their skills in selecting and using appropriate techniques and tools employed in data science and analytics to conduct a practical investigation into a data science and analytics problem. The project is a self-directed piece of work, conducted with minimum supervision that demonstrates the student's ability to plan and manage a substantial piece of work, and steer their own efforts. Students are expected to be thorough in their work, and, particularly, identify and tackle any difficult or challenging aspects of the problems they are trying to solve. It is not just the quantity, or even the quality of work that is considered when grading the project, but the level of difficulty and the scope of the problem being addressed.
Team Research and Development Project	15 Credits	Compulsory
Students working at, and beyond, Master's level are expected to understand both generic and domain-specific investigative methods, and to be able to apply them in their work. This module explores a range of such methods and helps students to enhance their proficiency in the skills that are expected of those working at postgraduate level. Furthermore, this module involves working actively as part of a team of fellow students on a complex, multi-domain computing problem. Typically, the project can be a research project to answer a research question, a thorough empirical investigation of a specific topic, or a development idea from student themselves, or a virtual or real client. Each team would be expected to manage the project, to report regularly to their supervisor(s) on the progress of the project, and to collectively deliver a set of appropriate outputs from the project. The output(s) of the team project will typically be a computing product or system and its presentation together with appropriate documentation.
Legal and Ethical Practice Exercise	15 Credits	Compulsory
The module will involve a broad case study involving the provisioning of a system which adheres to ethical and professional guidelines and standards. This will be a team project. One such case study might take the form of a large online platform where teams would have to create policies both for developers and users. The likely topics of those policies would be: issues involving sharing of user content, licensing of third party codes, moderation of content, consents from users relating to the tracking of user activity). The team would have to edit the platform such that all forms and submissions of user data are compliant with regulations particularly operative in the UK and the European Union.

Further course information

Course fact sheets
MSc Data Science and Analytics	Download

Programme specifications
MSc Computer Science (Modular)	Download

Additional information
Sandwich placement or study abroad year	n/a
Applications open to international and EU students	Yes

Student experience

At the University of Hertfordshire, we want to make sure your time studying with us is as stress-free and rewarding as possible. We offer a range of support services including; student wellbeing, academic support, accommodation and childcare to ensure that you make the most of your time at Herts and can focus on studying and having fun.

Find out about how we support our students

You can also read our student blogs to find out about life at Herts.

Funding and fees
Other financial support

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

Fees 2022
UK Students

Full time
- £9450 for the 2022/2023 academic year
Part time
- £790 per 15 credits for the 2022/2023 academic year
EU Students

Full time
- £16500 for the 2022/2023 academic year
Part time
- £1375 per 15 credits for the 2022/2023 academic year
International Students

Full time
- £16500 for the 2022/2023 academic year
Part time
- £1375 per 15 credits for the 2022/2023 academic year
Fees 2023
UK Students

Full time
- £9900 for the 2023/2024 academic year
Part time
- £825 per 15 credits for the 2023/2024 academic year
EU Students

Full time
- £15450 for the 2023/2024 academic year
Part time
- £1290 per 15 credits for the 2023/2024 academic year
International Students

Full time
- £15450 for the 2023/2024 academic year
Part time
- £1290 per 15 credits for the 2023/2024 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

Living costs / accommodation

The University of Hertfordshire offers a great choice of student accommodation, on campus or nearby in the local area, to suit every student budget.

View detailed information about our accommodation

Read more about additional fees in the course fact sheet

International/EU applicants without pre-settled status in the UK

Apply through our international/EU application portal

Home and EU applicants with pre-settled/settled status in the UK

2023

Start Date	End Date	Link
23/09/2023	30/09/2024	Apply online (Full Time)
20/01/2024	31/05/2025	Apply online (Full Time)
23/09/2023	30/09/2024	Apply online (Part Time)
20/01/2024	31/01/2025	Apply online (Part Time)

2024

Start Date	End Date	Link
23/09/2024	30/09/2025	Apply online (Full Time)
20/01/2025	31/05/2026	Apply online (Full Time)
23/09/2024	30/09/2025	Apply online (Part Time)
20/01/2025	31/01/2026	Apply online (Part Time)

MSc Data Science and Analytics

Why choose Herts?

What job can I get?

Work placement

Professional Accreditations

About the course

Why choose this course?

What will I study?

Further course information

Student experience

Other financial support

Fees 2022

UK Students

Full time

Part time

EU Students

Full time

Part time

International Students

Full time

Part time

Fees 2023

UK Students

Full time

Part time

EU Students

Full time

Part time

International Students

Full time

Part time

Living costs / accommodation

International/EU applicants without pre-settled status in the UK

Home and EU applicants with pre-settled/settled status in the UK

2023

2024