MSc Data Science and Analytics with Placement Year

Why choose Herts?

Industry Accreditation: Accredited by the British Computer Society (BCS) enabling graduates to register at The Chartered Institute for IT as a chartered IT professional.
Work Placement Opportunities: You will gain real industry experience during a one-year work placement.
Employment Prospects: Graduates in our department are now data scientists, data engineers, and data analytics consultants working for organisations including BT, IBM and Microsoft.

This course is up for periodic review
To ensure this course continues to be cutting-edge and enables you to be ready for the modern workplace, it is due to be reviewed by March 2025.

Our website will typically be updated within a month of the review confirming any enhancements, including:
- module titles (and whether they are core or optional)
- expected contact hours
- assessment methods
- staff teaching on the course

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	Sandwich, 2 Years
Location	University of Hertfordshire, Hatfield

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

The course 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.

We will provide excellent academic and personal support during both your academic and placement periods together with comprehensive career guidance from our very experienced dedicated Careers and Placements Service.

Although the responsibility for finding a placement is with you, our Careers and Placements Service maintains a wide variety of employers who offer placement opportunities and organise special training sessions to help you secure a placement, from job application to the interview. Optional one-to-one consultations are also available.

In order to qualify for the placement period you must pass all the first 60 credits of your study in your first attempt.

Professional Accreditations

Accredited by BCS, The Chartered Institute for IT for the purposes of partially meeting the academic requirement for registration as a Chartered IT Professional. Students on the specialist MSc with placement (Semester B entry) must pass the 7COM1064 Preparation for Placement module as a condition for accreditation.

About the course

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

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 your 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.

The programme offers three award routes that you can choose to study:

MSc Data Science and Analytics
MSc Data Science and Analytics with Placement Year
MSc Data Science and Analytics with Advanced Research

Why choose this course?

3rd for computer science in the Postgraduate Taught Experience Survey (PTES, Advance HE, 2023).
You will gain industrial experience by applying the skills you have learned throughout your studies in a practical environment.
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.

Where will I study?

Learn in our brand-new School of Physics, Engineering and Computer Science building, opening in 2024, where you’ll experience a range of experiential learning zones.

The computer science labs are home to telecommunications, robotics and UX empathy labs, with a variety of research spaces that range from dark rooms to clean rooms, and sample prep labs to calibration and assembly labs.

You will also benefit from a Success and Skills Support Unit, which is aimed at helping you build your employability and academic skills. Plus, have access to industry mentors who will provide you with pastoral support, vocational guidance, and career progression opportunities.

The new building will also provide space to collaborate, with plenty of workshops, social and meeting spaces available. Even better, the building has been designed with the University’s net zero carbon target in mind, and forms part of our plan to replace or upgrade older sites that are energy inefficient.

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/datasets. It is currently regarded as the key element of a much more elaborate process called Knowledge Discovery in Databases (KDD). The combination of 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 process 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.
Preparation for Placement	0 Credits	Compulsory
The module will explain the benefits of the Supervised Work Placement and encourage students to apply. It will support students in their application by informing them about the types of employer and job role available, helping them select the most appropriate for their strengths and weaknesses, and how employers conduct the recruitment process. The module will assist students to make an application, throughout the entire process, via a series of lectures, seminars, individual guidance and online communication. This includes writing of CVs and letters of application, development of interview technique and other forms of assessment. For those who are successful in securing a placement there will be further help in preparing for employment.
Foundations of Data Science	30 Credits	Compulsory
This module focuses on a range of topics related to basic mathematical concepts and skills, including linear algebra, calculus, statistics, probability, Bayesian inference, set theory, and information theory. The content may vary from year to year, but the aim is to apply the mathematical foundations 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. It focuses on the practical aspects of Data Science, for example (but not limited to) data retrieval and pre-processing, data analysis, modelling, and visualisation. We will study typical data science workflows in concrete use cases. Along the way, essential tools for data science are introduced. New developments in tools and techniques in data science will be covered as they emerge.
Data Science and Analytics Masters Project	60 Credits	Compulsory
The project is an opportunity for students to demonstrate what they know about current research and practices in computer science and apply their skills to a range of computer science topics in order to conduct a practical investigation of a particular computer science 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
This module explores a range of generic and domain"specific investigative 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 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 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.
Professional Work Placement for MSc Computer Science	60 Credits	Compulsory
Supervised work experience provides students with the opportunity to set their academic studies in a broader context, to gain practical experience in specific technical areas and to strengthen their communication and time"management skills. It greatly assists them in developing as independent learners, so that they will be able to gain maximum benefit from the learning opportunities afforded by their future study programme. It gives students opportunities, according to the nature of the placement experience, to acquire the basis of technical expertise in specialist areas, which they may be able to enhance through study after completion of the placement, especially in the final project.
Responsible Technology	15 Credits	Compulsory
This module is concerned with legal, social, ethical and professional issues that may affect the work of practitioners in the computing and technology sectors. Its main focus is on the ethical considerations inherent in the development of responsible technologies. Topics covered are likely to vary from year to year to reflect contemporary research and issues.

Further course information

Course fact sheets
MSc Data Science and Analytics (Sandwich)	Download

Programme specifications
MSc Data Science and Analytics - Extended	Download

Additional information
Sandwich placement or study abroad year	Mandatory
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 2024
UK Students

Full time
- £13545 for 2024/2025 and 2025/2026 inclusive
EU Students

Full time
- £18950 for 2024/2025 and 2025/2026 inclusive
International Students

Full time
- £18950 for 2024/2025 and 2025/2026 inclusive
*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

2024

Start Date	End Date	Link
25/09/2024	31/05/2025	Apply online (Full Time/Sandwich)
22/01/2025	31/01/2026	Apply online (Full Time/Sandwich)

2025

Start Date	End Date	Link
25/09/2025	31/05/2026	Apply online (Full Time/Sandwich)
22/01/2026	31/01/2027	Apply online (Full Time/Sandwich)

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MSc Data Science and Analytics with Placement Year

Why choose Herts?

What job can I get?

Work placement

Professional Accreditations

About the course

Why choose this course?

What will I study?

Where will I study?

Further course information

Student experience

Other financial support

Fees 2024

UK Students

Full time

EU Students

Full time

International Students

Full 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

2024

2025