The course responds to the growing:
demand for highly trained research scientists to design and implement data analysis pipelines for the increasingly large and complex data sets produced by the next generation of scientific experiments;
societal demand for data science and data analysis skills in the industry, especially when applied in strategic domains (science, health) and economic areas (finance, e-commerce);
need to train postgraduate students with a deep understanding of data science techniques and algorithm building for modern computer architectures and utilising industry best practices for software development;
importance of open science in research, specifically reproducibility of scientific results and the creation of public data analytic codes.
The course aims:
to give students deep knowledge of data science tools and techniques, including machine learning and AI, and the statistical frameworks underpinning them.
to give students knowledge and practical experience of modern software best practice and collaborative code development.
to give students an understanding and practical experience of the application of modern data-science methodologies in specific scientific domains utilising industry best practice.
to give students an awareness of the wide range of industrial applications of data science more broadly.
Learning outcomes
By the end of this course, students will have:
Knowledge and Understanding
1. Knowledge of statistical analysis and how to employ it in several practical domains.
2. A deep understanding of machine learning techniques and packages and how to apply them in different scientific areas.
3. Skills in writing sustainable and comprehensive codes in a collaborative framework, utilising modern software development best practice and how to share these publicly.
4. A practical understanding of the real-world application of data science tools to large- scale scientific problems.
5. Knowledge in data analysis for continuation of data driven PhD research in scientific areas.
6. Practical understanding of data analysis and research software development to facilitate their careers in relevant industries.
Skills and other attributes
1. Technical and methodological skills necessary to undertake data-intensive research in a chosen scientific area.
2. The capacity to assess the different machine learning and data science methodologies used in scientific analysis.
3. Skills in software development following best practice and tools for open science.
4. Collaborative skills, through working with other students on the practical exercises and lecture projects.
5. The ability to communicate their work clearly, both orally and in writing.
6. Broad experience in the application of Statistical and Machine learning techniques to scientific data.
Continuing
Students wishing to progress to PhD study after passing the Masters degree should apply for admission to a PhD through the University admissions website, taking the funding and application deadlines into consideration.
Open Days
The University hosts and attends fairs and events throughout the year, in the UK and across the world. We also offer online events to help you explore your options.
Discover Cambridge: Master’s and PhD study webinars
Practical, step-by-step information about preparing a strong application for postgraduate study. Find out more on the Discover Cambridge webpage.
Virtual Postgraduate Open Days
The November Open Days focus on subject and course information. Webinar recordings are available until May.
Events for international students
Join us for Q&A webinars, funding webinars or get in touch with our regional managers. Find out more about events for international students.
For more information about all upcoming events visit our events pages.
Departments
This course is advertised in the following departments: