Course summary
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. Learning Outcomes By the end of this course, students will have: thorough knowledge of statistical analysis including its application to research and how it underpins modern machine learning methods; comprehensive understanding of data science and machine learning techniques and packages and their application to several practical research domains; developed advanced skills in computer programming utilising modern software development best practices created in accordance with Open Science standards; demonstrated abilities in the critical evaluation of data science tools and methodologies for their real-world application to scientific research problems. 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.
Assessment method
Thesis / Dissertation Data Analysis Projects will primarily be concerned with the reproducibility of key scientific analysis. Projects will be marked on three aspects: the project reports, the accompanying data analysis pipeline developed for the analysis and the oral presentation of the project. The report must not exceed 7,000 words in length and describe the analysis pipeline and its development, and the project goals and results obtained. The report must be accompanied by an executive summary of the work of not more than 1,000 words in length. The data analysis pipeline used for the analysis must also be provided to the assessors in a form which is accessible and reproducible. The oral presentation will be used to confirm the candidates understanding of the project and to clarify any points which were unclear in the report or analysis pipeline. Assessors may ask questions of the candidate during the presentation to further explore any aspect of the project, the submitted materials, the presentation, or other background knowledge relevant to the project. Other Each Major and Minor Module will be assessed via a mix of: Coursework - which will typically be in the form of a report describing the development and implementation of specific data analytic methods, typically of not more than 3,000 words in length, in conjunction with the data analytic pipeline itself, however the exact form will be module dependent. The reports will be expected to be concise and will be judged on the quality of argument, the clarity of presentation, and the insightfulness of interpretation. The pipeline itself will be judged on conformity to software development best practice as taught in the 'Research Computing' major module, and the quality of the pipeline in terms of its accuracy, range of application, ease of use, and robustness and stability. Written exams - which will be closed books and will primarily test candidates' theoretical knowledge via calculations, short answer questions and essays. Oral presentations on candidates’ work may be required as part of the assessment of submitted coursework. In the MPhil in Data Intensive Science, the weighting of the assessed course components is as follows: the Project report (Data Analysis project) will represent 25 per cent (25%) of the final grade; the taught modules examination (mix of written assignment, written examination, and oral presentation) will represent 75 per cent (75%) of the final grade where : Each major module will count for 12% of the final grade. Each minor module will count for 7.5% of the final grade.
Entry requirements
Applicants for this course should have achieved a UK Good II.i Honours Degree. If your degree is not from the UK, please check International Qualifications to find the equivalent in your country. Applicant’s degree should be in science or a technology discipline, and applicants are expected to demonstrate abilities at an adequate level in mathematics especially in the domain of linear algebra, statistics and probability.
Fees and funding
Tuition fees
No fee information has been provided for this course
Tuition fee status depends on a number of criteria and varies according to where in the UK you will study. For further guidance on the criteria for home or overseas tuition fees, please refer to the UKCISA website .
Additional fee information
Provider information
University of Cambridge
The Old Schools
Trinity Lane
Cambridge
CB2 1TN