MSc Data Science and Big Data Analytics

What you will study

The MSc Data Science and Big Data Analytics program aims to cultivate graduates who possess expertise in the field of data science. Encompassing a broad spectrum of subjects, the program delves into areas such as machine learning techniques, implementation and assessment of data science methodologies, utilisation of analytical tools, and exploration of big data analytics.

YEAR 1

Modules

• Applied Data Science: To provide a hands-on understanding of various data analysis tools, programming tools, and techniques. It focuses on building proficiency in data cleaning, visualization, modelling and machine learning enable. It also encourages students to apply data science methods to different fields such as business, healthcare, finance or social sciences, enabling them to make informed decisions and derive actionable insights in a specific context.
• Advanced Data Analysis and Visualisation: To explore the advanced concepts of collecting, analysing and visualising data and to create data analysts who can identify patterns and display information from data of several sources. You will explore various statistical methods and algorithms for data analysis. You will also be able to discover, analyse, visualise, and present data in a meaningful way that will harness the power of data for new insights and evaluate the legal, social and ethical impact of data analysis.
• Advanced Data Structures and Algorithms.docx: This module will give students a thorough grounding in the theories and application of key computer programming concepts such as algorithms, abstract data types, underlying data structures and their integration to produce efficient code. This allows you to develop the knowledge and skills to be able to analyse problems and then design, implement, and analyse, effective algorithmic solutions using a suitable programming language.
• Advanced Machine Learning: You will be introduced to the practical challenges of applying machine learning techniques to real-world problems. Building on existing programming knowledge, you will gain a broad understanding of the key concepts, methodology and techniques required to develop effective algorithms to analyse large data sets. This will be combined with the analysis techniques required to compare, select and justify the use of appropriate machine learning methods whilst developing programmed solutions.
• Database Systems and Data Analytics: You will develop an understanding of the role of database systems in Information Management, and the theoretical and practical issues that influence the design and implementation of database management systems. It will provide the student with the skills required to create, maintain, and interrogate a relational database management system using commercially available database software. You will also develop knowledge of database systems and data analytics.
• Research Methods for Digital Technologies: The module will provide the necessary underpinning skills to ensure that competent work and standards are achieved and maintained throughout the student’s chosen programme of study. This will encompass the development of professional level information handling and analysis skills, as well as ensuring students become proficient at planning and managing their own research projects.
• Dissertation Project: This module will support and aid students in carrying out an independent research project based within their area of study.

Entry requirements & applying

Normal entry requirements for full time and part time will be one of:

• An honours degree with a 2:2 classification in any subject area
• Academic qualifications in other subject areas or at a lower level than honours degree but supported by a maturity of experience at a professional level in a relevant specialist area
• Equivalent qualifications of another overseas country which are deemed satisfactory by the program team

Teaching & Assessment

Teaching 

The computing program suite employs a diverse range of cutting-edge industry tools and software, complemented by innovative teaching methods. This dynamic approach not only imparts industry-relevant skills but also empowers you to elevate your work to new heights when possible.

Assessment 

Assessments in Data Science and Big Data Analytics at university level are designed to evaluate your understanding, application, and proficiency in various aspects of the discipline. These assessments encompass a diverse range of methods, including:

• Coursework and Projects: Assignments and projects provide hands-on experience, allowing you to apply theoretical knowledge to real-world scenarios. This may include software development projects, research papers, or problem-solving tasks. 
• Coding Assignments: Practical coding assignments assess your programming skills, logical reasoning, and ability to develop efficient and effective code. 
• Group Projects: Collaborative projects evaluate teamwork, communication, and the ability to work in diverse teams, reflecting the collaborative nature of the tech industry. 
• Presentations: You may be required to present your findings, solutions, or project outcomes, enhancing your communication and presentation skills. 
• Laboratory Work: Practical sessions in computer labs assess your ability to apply concepts, troubleshoot issues, and work with various tools and technologies. 
• Problem-solving Exercises: These exercises challenge you to solve complex problems, encouraging critical thinking and analytical skills. 
• Reports and Documentation: Writing reports or documenting project processes assesses your ability to communicate technical information clearly and concisely. 

Personalised Support      

The department follows a well-established open-door approach, actively interacting with students, alumni, and industry stakeholders. Essential information and communication avenues are facilitated through tools like Teams and Moodle. Additionally, every student is assigned a personal tutor, fostering regular meetings, while additional personalized support is extended to part-time students through the Virtual Learning Environment (VLE). 

Career prospects

One obvious advantage of a Data Science and Big Data Analytics master’s degree is that students become more employable. Jobs include, but are not limited to: 

• Data Scientist
• Data Analyst
• Intelligence Analyst
• Machine Learning Engineer
• Big Data Engineer
• Predictive Analyst
• Research Scientist

Fees & funding

You do not have to pay your tuition fees upfront.

The fees you pay and the support available will depend on a number of different factors. Full information can be found on our fees & finance pages. You will also find information about what your fees include in the fee FAQs.

All fees are subject to any changes in government policy, view our postgraduate fees.

International

This course is open to international students, for information about the university’s entry requirements for EU/international students, please visit our international section.