Data Science MSc (with optional Placement/Project year)

Program Overview

This conversion Master's degree in Data Science equips students with the skills needed to excel in the growing field of data science. Designed for individuals with a technical background, the program covers topics such as data analytics, machine learning, and Python for data science. Students can choose to complete a project or placement year, providing hands-on experience in the industry. Upon graduation, graduates will be prepared for careers in various sectors, including finance, research, and technology.

Program Outline

Degree Overview:

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 programme in data science. This programme is ideal for those interested in data analytics, machine learning, statistics and Python for data science. The Data Science course is within the Department of Computer Science, which is a forward-thinking innovative department. 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 Department'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.

Outline:

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.

Modules:

• Enterprise Development (Compulsory): This module introduces students to key approaches, behaviours and skills of successful business enterprise by providing insight into real world business scenarios, key tools and processes required for both developing an existing business and creating a new business venture start up.
• Statistical Programming (Compulsory): The module introduces the basic concepts or programming for the purpose of statistical analysis.
It explores data structures and functions that may be used to store and analyse data.
• Research Dissertation (Compulsory): Projects which will involve the application of methods and equipment introduced in taught modules, will be based on subjects agreed in principle with the Postgraduate Dissertation Coordinator and potential supervisors.
The research dissertation may be University-based or carried out in the employer’s workplace, or through a work placement where a local organisation has a direct role in facilitating the project.
• Research Methods (Compulsory): To include: time management, library skills and literature search, evaluation of information sources, critical analysis of information, 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, research approaches and methodologies, design and application of questionnaires & interviews, quantitative and statistical tools for researchers (e.g.
R, Python, SPSS).
• Principles of Data Science (Compulsory): This module investigates tools and techniques to extract, transform and load (ETL) data into a data warehouse for the purpose of online analytical processing (OLAP).
Students will be guided through step-by-step demonstrations showing them how to perform the ETL process using a suitable tool, such as Python or R. Tools such as SQL and Excel will be used to demonstrate extracting data for visualisation and analysis, e.g., building a data cube. Additionally, the module will investigate alternative approaches to data warehousing, e.g., the Hadoop ecosystem.
• Machine Learning (Compulsory): 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 on data using specified libraries in either Python or R. Students will be required to investigate the merits of each algorithm for various types of data in both theory and practice.
• Ethics of Data Science (Compulsory): This module aims to introduce students to ethics as applied to the fields of data science, machine learning and artificial intelligence.
It explores these theories in the context of real world problems including accountability, ethical availability of data, enhancing the value of data, and ensuring accountability, transparency and oversight.

Assessment:

The majority of work will be assessed by coursework.

Teaching:

The course uses a variety of teaching methods, including:

• Lectures
• Workshops
• Seminars
• Research

Careers:

Tuition Fees and Payment Information:

Home Students

• £10,215 per year (2024/25)

International/EU Students

• £14,750 per year (2024/25)
• The professional placement/project year will cost an additional £2,650 (due at the start of the second year of the course), totaling £17,400 for the full course fee 2024/25.
• The University of Chester offers generous international and merit-based scholarships for postgraduate study, providing a significant reduction to the published headline tuition fee.
You will automatically be considered for these scholarships when your application is reviewed, and any award given will be stated on your offer letter.

Additional Costs

• Your course will involve additional costs not covered by your tuition fees.
This may include books, printing, photocopying, educational stationery and related materials, specialist clothing, travel to placements, optional field trips and software. Compulsory field trips are covered by your tuition fees.
• If you are living away from home during your time at university, you will need to cover costs such as accommodation, food, travel and bills.