Coventry University - Data Science MSc

Coventry University

Data Science MSc

Data is everywhere. As the volume and complexity of data collected continue to grow, there is increasing demand for expertise in data science to support the analysis and visualization 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 students with little previous experience of data analysis or computer programming and to help them to gain new skills such as working with databases; statistical thinking; programming in high-level languages; modeling; applying data science tools and packages; machine learning; information retrieval; data visualization and addressing the challenges of big data.

Entry Requirements

An applicant will normally be expected to possess at least one of the following:

  • A good honours degree or equivalent qualification.
  • An unclassified degree in a relevant field plus professional experience.

In addition, applicants will need a little knowledge of computer programming. Applicants from non-programming backgrounds are encouraged to take part in a free online course* with the aim of bringing their knowledge of programming and basic data science topics up to the required level for successful application.

IELTS: 6.5 overall, with no component lower than 5.5. If you don't meet the English language requirements, you can achieve the level you need by successfully completing a pre-sessional English programme before you start your course. 

Career Prospects

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.

Course Details

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 information retrieval, 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; information retrieval and analysis of textual data; machine learning algorithms for learning from data; and a range of data science applications, tools, projects and current issues.

Year One

• Programming for Data Science
• Principles of Data Science
• Big Data Analytics and Data Visualisation
• Data Management Systems
• Information Retrieval
• Machine Learning
• Global Professional Development – Entrepreneurial Practice
• Data Science Project

*The information’s are correct at the time of publishing, however it may change if university makes any changes after we have published the information. While we try our best to provide correct information, It is advisable to call us or visit university website for up to date information.

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