Data Science (Conversion) MSc | University of Chester

This conversion Master’s degree in Data Science enables students to make the leap to the fast-growing area of data science.

The Master's degree in Data Science is designed for individuals with a technical, mathematical or engineering background who wish to enhance their skills in the field of data science. Students who have previously worked in these areas, but lack a formal degree, are also encouraged to apply to this Master's course in data science. This course is ideal for those interested in data analytics, machine learning, statistics and Python for data science.

The Data Science course is within the School of Computer and Engineering Sciences, which is a forward-thinking innovative School.

We work with employers to tailor the course to real-world needs, giving students an in-depth knowledge in the area.

The course content is cutting edge, building upon the School's expertise. This is reflected in modules such as statistical programming, machine learning, enterprise development and principles of data science.

There is an option to choose a Project/Placement year for this course, at an additional cost.

Why You'll Love It

Access to specialist computing labs with GPU resources.

computing students sitting in forun of a pc in a classrooom

Academic Skills Support

Guest speakers and employer involvement opportunities.

What You'll Study

The technical core modules cover an introduction to the subject, mathematical and statistical skills needed in data science, and more advanced techniques in machine learning and principles of data science.

You will take modules in the societal context and industry and entrepreneurial opportunities existing in the science of data.

If you choose a placement or project year, the Research Dissertation module will be replaced by a placement or project module.

Core Modules

An essential skill for postgraduate students is the ability to investigate topics with the objective of identifying facts , theories, ideas, methodologies, etc., that could inform the development of new insight for further research. A major aspect of this is critical analysis of information. The module aims to develop critical reasoning in students and an understanding of other researchers’ work. Students will learn how to use current research literature and relevant sources to gain new insight for a new research. They will learn how to support their research report with relevant facts, theories, ideas, etc. They will develop their ability to understand approaches and methodologies adopted in existing research toward writing a literature review and handling full research projects in their subject area.

The learning content also includes:

Time management, library skills and literature search
Evaluation of information sources
Ethical issues in science, technology and engineering research (including intellectual property and plagiarism)
Writing for research: styles and rules for presentation (including referencing standards)
Choosing a research area and evaluating source material
Hypothesis formation
Design and application of questionnaires & interviews
Quantitative and statistical tools for researchers (e.g. R, Python, SPSS)

The module introduces the basic concepts of programming for the purpose of statistical analysis. It explores data structures such as lists, dictionaries, and arrays and functions to calculate min, max, mean, and standard deviation.

The mathematical and statistical skills include statistics and probability, multivariate calculus, linear algebra and optimisation methods.

The topics covered include:

Programming Concepts

Data structures: lists, dictionaries, arrays.
Functions: min, max, mean, standard deviation.
Programming logic and debugging.
Data visualization: Creating visualizations using tools like matplotlib, ggplot2, and seaborn.
Big data concepts: Basics of working with large datasets and addressing scalability challenges.

Statistics

Descriptive statistics.
Probability theory.
Inferential statistics.
Exploratory data analysis.
Probability distributions: Understanding normal, binomial, and Poisson distributions and their applications.
Time series analysis: Introduction to time series methods such as moving averages and ARIMA.Linear Algebra

Linear Algebra

Vectors and matrices.
Eigenvalues and eigenvectors.
Linear systems.

Optimisation Methods

Unconstrained optimisation.
Constrained optimisation.
Applications in statistical modelling and machine learning.

This module investigates tools and techniques to extract, transform and load (ETL) data for further analyses or analytical processing (OLAP). Students will be guided step-by-step through the ETL process using Python, API's and SQL to show visualisation and analysis. The module will also discuss the ethical implications of data, data processing, laws and standards.

This module investigates different types of machine learning algorithms to find patterns in data. Each algorithm will be discussed in theory and practice, discussing: its data pre-processing requirements, pseudo-code, and evaluation metrics, e.g., Dunn index for clustering. Detailed demonstrations will show how to apply these algorithms to data using specified libraries in Python. Students will be required to investigate the merits of each algorithm for various types of data in both theory and practice.

The databases and security module involves the development of databases and their querying through the use of SQL. Databases will be discussed both theoretically and in practice. Students will have opportunies to develop and practice database creation and development. Database security will be discussed and shown how to apply in practice.

The purpose of this module is to provide a solid foundation in programming for students with a range of academic backgrounds. It provides a solid foundation in computer programming for students from a variety of academic and professional backgrounds. It is designed to equip students with the essential skills to become a proficient coder. From the fundamental building blocks of programming, including a deep dive into object-oriented programming, to advanced concepts, students are guided each step of the way. As part of the learning experience, students will learn how to create efficient and well-structured code that solves real-world problems. By the end of this module, students will have a solid foundation in programming and be equipped to tackle various coding challenges.

Optional Modules

The Research Project is the pinnacle of a taught, academic course of master’s level study. It is a demonstration of academic, subject-specific, and research capabilities. Projects are a significant and substantial piece of individual work that draw upon the knowledge, technical abilities, and problem-solving skills developed in earlier modules. Students need to apply high-level research skills to a defined, complex problem. A distinguishing feature of the research project is that it is largely self-directed and independent, with support from an academic supervisor.

The information listed in this section is an overview of the academic content of the course that will take the form of either core or option modules and should be used as a guide. We review the content of our courses regularly, making changes where necessary to improve your experience and graduate prospects. If during a review process, course content is significantly changed, we will contact you to notify you of these changes if you receive an offer from us.

If chosen, your second year of study will consist of your Project or Professional Placement

This module forms part of the 2 year pathway along with a preparation module - WB7102 Advance Professional Practice. The preparation module forms part of year 1 study and is completed before this module commences.

Students studying the 2 year full time course will not undertake a traditional dissertation module this module replaces that option instead, this module is completed in Year 2, starting at the beginning of year 2, for a minimum of 30 weeks over 3 terms on a full time basis with the exception of university scheduled vacations which equates to a minimum of 1000 work based project hours.

Students studying the 2 year full time course will not undertake a traditional dissertation module this Project module replaces that option instead, this module is completed in Year 2, starting at the beginning of year 2, for a minimum of 30 weeks over 3 terms on a full time basis with the exception of university scheduled vacations which equates to a minimum of 1000 work based project hours.

How you'll Learn

How we teach at the University of Chester

Teaching

The course uses a variety of teaching methods, including:

Lectures
Workshops
Seminars
Research

The course consists of six 20-credit modules and a 60-credit supervised research module.

Assessment

The majority of work will be assessed by coursework.

Your Future Career

Job Prospects

Students will be able to pursue careers in the field of data science in a number of industry areas, including: finance, scientific research, retail, information technology, government, ecommerce and many more.

Graduates may progress into roles such as Data Analyst, Junior Data Scientist, Machine Learning Engineer, Data Engineer, BI Developer, and Analytics Consultant. You’ll develop skills to build data pipelines, train and evaluate models, communicate insights effectively, and work confidently with SQL and APIs.

The course also helps you build evidence of your skills through assessed projects that can support job applications.

Careers service

The University has an award-winning Careers and Employability service which provides a variety of employability-enhancing experiences; through the curriculum, through employer contact, tailored group sessions, individual information, advice and guidance.

Careers and Employability aims to deliver a service which is inclusive, impartial, welcoming, informed and tailored to your personal goals and aspirations, to enable you to develop as an individual and contribute to the business and community in which you will live and work.

We are here to help you plan your future, make the most of your time at University and to enhance your employability. We provide access to part-time jobs, extra-curricular employability-enhancing workshops and offer practical one-to-one help with career planning, including help with CVs, applications and mock interviews. We also deliver group sessions on career planning within each course and we have a wide range of extensive information covering graduate jobs and postgraduate study.

Entry Requirements

Honours Degree

A Bachelor's degree – 2:2 or above. However, relevant work experience will also be considered.

Students from countries outside the UK are expected to have entry qualifications roughly equivalent to UK A Level for undergraduate study and British Bachelor's degree (or equivalent) for postgraduate study. To help you to interpret these equivalents, please select your country/region of residence to see the corresponding entry qualifications, along with information about your local representatives, events, information and contacts.

For more information on our English Language requirements, please visit International Entry Requirements.

IELTS	6.5 (Min 5.5 in each sub-skill)
Honours Degree	A Bachelor's degree – 2:2 or above. However, relevant work experience will also be considered.

Optional 2-year Master's To Suit Your Needs

Choosing a Professional Placement MSc is a win-win for your career, giving you the chance to get real experience, apply your cutting-edge skills in the workplace and stand out to future employers.

In the first year you will have help from the University to find a placement, whilst developing your expertise. You will then spend your second year out in industry on placement, getting the chance to work with industry professionals and grow your network of industry contacts. Bringing your university-acquired knowledge and insights to industry, you will get to make a difference to the workplace and make lasting links with your employer.

Students need to find and secure their own placement, supported by the University. A preparation module will also help you to get ready for your placement.

Please note, this course is available as a one-year master degree course, or as a Two-Year Master's Course with a Professional Placement or Project. Please carefully consider your options when applying for our one year or Two-year routes as successful international applicants will not be able to change between courses after a Confirmation of Acceptance of Studies (CAS) letter has been issued or after arriving in the UK.

Fees and Funding

£10,890 for a full-time course (2026/27)

The tuition fees for home students studying courses in 2026/27 are £10,890 per year.

The professional placement/project year will cost an additional £2,750, due at the start of the second year of the course.

Guides to the fees for students who wish to commence postgraduate courses are available to view on our Postgraduate Fees page. Here you will also find information about part-time fees and project/placement year fees.

£15,500 (2026/27)

The tuition fees for international students studying courses in 2026/27 are £15,500 per year.

For courses where a placement or project year is undertaken there will be an additional charge of £2,750 for the placement/project year (due at the start of the second year of the course).

Irish Nationals living in the UK or ROI are treated as Home students for Tuition Fee Purposes.

For more information about International fees for Postgraduate study, go to our International Fees, Scholarship and Finance section.

Data Science (with optional Placement/Project year) MSc

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