(MSc) Master of Science
Data Science with Professional Placement
Current
University of Essex
University of Essex
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Masters
Full-time
None
MSC G304PP
10/05/2023
Details
Professional accreditation
None
Admission criteria
We will consider applicants with a 2:1 degree in one of the following subjects:
- Mathematics,
- Statistics
- Operational research
- Computer Science
- Applied Mathematics
- Pure Mathematics
- Biostatistics
- Economic Statistics
- Statistics
- Economics
OR
A 2.1 degree in any subject which includes:
One module in:
- Calculus
- Maths
- Engineering Maths
- Advanced Maths
And one module in
- Statistics or Probability
- Maths
- Engineering Maths
- Advanced Maths
And one additional relevant module, from
- Algebra
- Analysis
- Programming language (R, Matlab or Python)
- A second module in Probability or Statistics
- Numerical Methods
- Complex Numbers
- Differential Equations
- Optimisation (Linear Programming)
- Regression
- Stochastic Process
- Maths
- Engineering Maths
- Advanced Maths
Applicants with a degree below 2:1 or equivalent will be considered dependent on any relevant professional or voluntary experience and previous modules studied.
IELTS (International English Language Testing System) code
IELTS 6.0 overall with a minimum component score of 5.5
If you do not meet our IELTS requirements then you may be able to complete a pre-sessional English pathway that enables you to start your course without retaking IELTS.
Additional Notes
The University uses academic selection criteria to determine an applicant’s ability to successfully complete a course at the University of Essex. Where appropriate, we may ask for specific information relating to previous modules studied or work experience.
Course qualifiers
A course qualifier is a bracketed addition to your course title to denote a specialisation or pathway that you have achieved via the completion of specific modules during your course. The
specific module requirements for each qualifier title are noted below. Eligibility for any selected qualifier will be determined by the department and confirmed by the final year Board of
Examiners. If the required modules are not successfully completed, your course title will remain as described above without any bracketed addition. Selection of a course qualifier is
optional and student can register preferences or opt-out via Online Module Enrolment (eNROL).
None
Rules of assessment
Rules of assessment are the rules, principles and frameworks which the University uses to calculate your course progression and final results.
Additional notes
None
External examiners
External Examiners provide an independent overview of our courses, offering their expertise and help towards our continual improvement of course content, teaching, learning, and assessment.
External Examiners are normally academics from other higher education institutions, but may be from the industry, business or the profession as appropriate for the course.
They comment on how well courses align with national standards, and on how well the teaching, learning and assessment methods allow students to develop and demonstrate the relevant knowledge and skills needed to achieve their awards.
External Examiners who are responsible for awards are key members of Boards of Examiners. These boards make decisions about student progression within their course and about whether students can receive their final award.
Programme aims
The course aims to educate and to train data scientists, who are in demand for a fast growing number of jobs and opportunities within the private and public sector. The theory and methodology of Data Science can be described as an intersection of computer science, statistics and operations research. The application of theory and methods to real world problems and applications is at the core of Data Science, which aims especially to use and to exploit Big Data, where appropriate and useful.
The course is for students, who are interested in solving real world problems, like to develop skills to use smart devices and computer science efficiently, want to use and to foster understanding of mathematics and are interested and keen to use statistical techniques and methods to interpret data. A balance of solid theory and practical application, this course builds on knowledge in relevant areas of computer science, statistics, data analysis and probability.
If a student has a weak background in statistics, the course offers a conversion module to assist the student to get sufficient knowledge. If a student has a weak background in programming, the course offers a conversion module to assist the student to get sufficient knowledge.
Learning outcomes and learning, teaching and assessment methods
On successful completion of the programme a graduate should demonstrate knowledge and skills as follows:
A: Knowledge and understanding
A1: Knowledge and understanding of some statistical modelling methods and techniques, such as sampling schemes, multivariate techniques, regression modelling and stochastic modelling.
A2: Knowledge and understanding of some data analysis methods for analysing statistical models, such as graphic visualisation, statistical estimation and hypothesis test.
A3: Knowledge and understanding of how to develop and to apply machine learning methods.
A4: Knowledge and understanding of how to develop and to apply text analytics methods.
A5: Knowledge and understanding of how to apply computational and statistical techniques into different areas, such as business, economics, finance, epidemiology and medical research.
A101: The ability to engage in a self-reflective process of adapting their taught skills to a work environment.
Learning methods
Lectures are the principal method of delivery for the concepts and principles involved in A1 – A5. Students are also directed to reading from textbooks and material available on-line. In some modules, understanding is enhanced through the production of a written report.
Understanding is reinforced by means of classes (A1 – A5), laboratories and assignments (A1 – A5).
Assessment methods
Achievement of knowledge outcomes is assessed primarily through unseen closed-book examinations, and also, in some modules, through marked coursework, laboratory reports, statistical assignments, project reports and oral examinations (A1-A5).
Regular problem sheets provide formative assessment in all modules.
Methods employed to assess knowledge and understanding statistics include: class presentations; written coursework, project work and class tests.
B: Intellectual and cognitive skills
B1: Identify an appropriate analytical, mathematical and/or statistical model for a specific quantitative question.
B2: Analyse a given quantitative problem and select the most appropriate computational and statistical tools for its solution.
B3: Use critical computational, statistical and mathematical properties to evaluate statistical methods and results.
B4: Perform analytical and statistical data analysis.
B5: Use analytical and statistical models and methods for research strategies.
Learning methods
The basis for intellectual skills in in computer science, statistics and operations research modules is provided in lectures, and the skills are developed by means of recommended reading, guided and independent study, assignments and project work.
B1 - B4 are developed through exercises supported by classes.
B1 - B4 are all-important aspects of the projects that constitute a part of some modules, and the optional final year project.
B5 is acquired through MA981.
Assessment methods
Achievement of intellectual skills in computer science, statistics and operations research modules is assessed primarily through unseen closed-book examinations, and also through marked assignments and thesis project work.
Methods employed to assess knowledge and understanding computer science, statistics and operations research include: presentations; written coursework, project work and class tests.
C: Practical skills
C1: Use programming languages and computational tools and packages.
C2: The ability to apply a rigorous, analytic, highly quantitative approach to a problem.
C3: Managing, organising and presenting data.
C4: Gathering and processing information from different sources.
C5: Searching literature.
C6: Preparing report.
Learning methods
The practical skills of computer science, statistics and operations research are developed in exercise classes, laboratory classes, assignments and project work.
C1 is acquired through the learning of at least one programming language and the use of a number of computer packages, as a part of the teaching of modules for which they are relevant.
C2 is acquired and enhanced throughout the course.
C3 is acquired through such methods as group discussion of topical themes and analysis of authentic materials in class; laboratory work involving use of dedicated software and Web materials; and staff advice, feedback and interaction with students.
C4 is acquired and enhanced throughout the course.
C5-C6 are acquired and enhanced through MA981.
Assessment methods
Achievement of practical skills C1 and C2 is assessed through marked coursework and project reports.
Methods employed to assess practical skills C3 and C4 typically include: written coursework; written exams; class tests; web-based assignments.
Methods employed to assess practical skills C5-C6 include writing thesis reports.
D: Key skills
D1: Writing statistical arguments, ideas, outputs and other information clearly into a report.
D2: Use appropriate IT facilities as a tool for developing computer programmes, data analysis and in the analysis of mathematical problems, word processing, finding modern language materials etc.
D3: Use and interpret statistical techniques correctly.
D4: Analyse complex problems and find effective solutions.
D5: Improve own learning and performance from feedback.
D6: Working autonomously showing organisation, self-discipline and time management
D101: The ability to engage in a self-reflective process of adapting their taught skills to a work environment.
Learning methods
D1 is practised throughout the course in the writing of thesis reports, solutions to mathematical problems.
D2 is developed through the use of programming languages and computer packages in a number of statistics modules.
D3 and D4 are developed and enhanced in all statistics modules.
D5 is developed in various statistics modules, through exercises and assessments.
D6 is developed and enhanced throughout the course.
Assessment methods
D1 is assessed through coursework.
D2 is assessed primarily through coursework.
Assessment of the key skills D3 and D4 is intrinsic to subject based assessment in statistics.
D5 is assessed through learning feedbacks from student’s supervisor in the period of thesis writing.
Assessment of key skill D6 is mainly through successful submission of coursework etc.