Data Science (MSc)

Data is everywhere. As the volume and complexity of data collected continues to grow, there is increasing demand for expertise in data science to support the analysis and visualisation of all this information. 

The MSc Data Science is a conversion course for graduates from a wide range of disciplines and backgrounds looking to pursue a career, or upskill, in this new and rapidly developing field. Data Scientists are in short supply and there is high demand for data science skills across sectors including business, government, healthcare, science, finance, and marketing.

The aim of the MSc in Data Science is to support you if you have little previous experience of data analysis or computer programming. It aims to help you gain new skills such as:

• Working With Databases
• Statistical Thinking
• Programming In High-level Languages
• Modelling
• Applying Data Science Tools And Packages
• Machine Learning
• Data Visualisation
• Addressing The Challenges Of Big Data.

Why you should study this course

• If you are curious about applying data to interesting questions, have an eye for data visualisation, are talented at communicating with experts or the public, or take logical approach to solving problems, then you could make a great member of a data science team in any organisation.
• This course has been designed specifically for graduates from a diverse range of disciplines to give them an opportunity to gain expertise from across the broad field of data science to help complement their existing skills and knowledge.The course aims to develop foundational skills in computer programming, data analysis, and mathematics, for those with little previous experience. These skills are developed in context alongside hands-on use of data science software tools across a range of common applications.The skills gained upon successful completion of this conversion course should complement existing knowledge and skills from your undergraduate study or work experience, such as formulating questions, building arguments, writing reports, delivering presentations, creative problem solving, and a curiosity about data.Features of the course include working with other students with different experiences and perspectives and having the opportunity to gain broad experience of working with a variety of types of data to address diverse and interesting questions.

Data Science is a broad multidisciplinary field encompassing everything from cleaning and managing data to data visualisation and deploying predictive models.

• The course supports students from diverse backgrounds to develop the necessary foundations of data science in computer programming, data analysis and statistical thinking, before building more specialised knowledge and skills in data management, machine learning, and the technological challenge of dealing with big data. Throughout the course there are many opportunities for you to build on your existing knowledge and experience from your undergraduate degree or workplace, and gain experience in the analysis of data of a variety of kinds and sizes.
• The course maintains a balance between hands-on technology-dependent practical skills using modern software, knowledge and understanding of specialist methods and algorithms in learning from data, mathematical language and foundations, and broader issues around data ethics, data protection and communication with stakeholders of all kinds. In particular, the course covers: programming and software development in a high-level programming languages such as Python and R; data analytics, statistical modelling and programming with data; mathematical foundations of data science such as modelling, linear algebra, and probability; data management systems for structured and unstructured data; big data management, distributed databases and data visualisation; machine learning algorithms for learning from data; and a range of data science applications, tools, projects and current issues.

This MSc Data Science course is composed of a combination of modules that cover a broad range of data science methods, applications, and foundations.

• In the first semester, you will study two 30-credit modules which aim to introduce you to data science, provide you with an opportunity to develop skills in computer programming, build expertise in data analysis, and establish mathematical foundations. Additional one-to-one support is available through the sigma Mathematics and Statistics Support Centre (subject to availability).
• In the second semester, you will study four 15-credit modules which aim to broaden your knowledge in the application areas in data management systems, machine learning and big data. These modules respond to different challenges in data management and data analysis. Within these modules, a wide range of types and scales of data and data analysis methods will be introduced and applied, from supervised and unsupervised learning to the analysis of text documents.
• In the final semester, you will be expected to apply the knowledge and skills you have learned in the first two semesters by undertaking an in-depth individual Data Science project. This may be on some current issue or challenging application in data science and could be industry-based or undertaken in collaboration with one of the university research groups. Guided by a university tutor, this project helps you to develop your research and practical skills while also gaining professional Data Science experience.

Teaching methods may include a combination of:

• Lectures
• Problem-solving workshops
• Supervised computer laboratory sessions
• In-class discussions and presentations
• Directed readingformative assessments.

Teaching contact hours

• A 15-credit module will typically have approximately 30 hours of contact time associated with it, while a 30-credit module will have approximately 60 hours of contact time. This may include a combination of, but may not be limited to, lectures, group sessions, laboratory sessions and support sessions. In addition, you will be expected to undertake significant self-study of around 25-35 hours each week depending on the demands of individual modules.
• The contact hours may be made up of a combination of face-to-face teaching, individual and group tutorials, and online classes and tutorials.
• As an innovative and enterprising institution, the university may seek to utilise emerging technologies within the student experience. For all courses (whether on-campus, blended, or distance learning), the university may deliver certain contact hours and assessments via online technologies and methods.
• Since COVID-19, we have delivered our courses in a variety of forms, in line with public authority guidance, decisions, or orders and we will continue to adapt our delivery as appropriate. Whether on campus or online, our key priority is staff and student safety.

Assessment

• This course will be assessed using a variety of methods which could vary depending upon the module. Assessment methods may include coursework, tests, reports, projects, group work, presentations, and formal examinations.
• The Coventry University Group assessment strategy ensures that our courses are fairly assessed and allows us to monitor student progression towards achieving the intended learning outcomes.

Careers and opportunities
Upon successful completion of this course, you should be able to:

• Demonstrate systematic knowledge and critical understanding of core and advanced topics in data science and its theoretical foundations.
• Design and evaluate computer systems for the storage, organisation, management, retrieval and processing of different types of information and sizes of datasets, including distributed systems.
• Use an analytical approach, statistical thinking and a comprehensive understanding of appropriate models, methods, algorithms and software tools to analyse data of a variety of types, and identify the limitations of any analysis.
• Demonstrate practical skills and capabilities related to employment, including working effectively and constructively as part of a team, leading a team, motivating and communicating complex ideas accurately to experts and non-experts, and technical expertise with modern data science tools and technologies.
• Identify and apply appropriate practices within a professional, legal, social, cultural and ethical framework, including complex, inter-related, multi-faceted issues that can be found in a variety of organisations and professional contexts.
• Apply research skills such as planning research, and critical analysis of information from appropriate sources, demonstrate awareness of current issues and show originality in the application of knowledge where appropriate.
• Data Scientists are in short supply and there is high demand for data science skills across business, government, healthcare, science, finance, and marketing (to name a few).
• Successful graduates of this course should be well prepared to join a team in an organisation related to their undergraduate discipline (contributing data analytics skills alongside their subject knowledge) or a specialist data science team in a more general organisation or consultancy.
• Coventry University is committed to preparing you for your future career and giving you a competitive edge in the graduate job market. The university's Talent Team provide a wide range of support services to help you plan and prepare for your career.