(MSc) Master of Science
Data Science and its Applications
Current
University of Essex
University of Essex
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Masters
Part-time
MSC G30624
10/05/2023
Details
Professional accreditation
None
Admission criteria
A 2.2 degree in:
- Biostatistics
- Economics
- Economic Statistics
- Finance
- Marketing
- Physics
- Chemistry
- Biology
- Applied Mathematics
- Pure Mathematics
- Statistics
- Operational Research
- Computer Science
OR
A 2:2 degree in any subject which includes:
One module in:
Calculus (AKA Mathematical Analysis)
Maths (I/II/III)
Engineering Maths (I/II/III)
Advanced Maths (I/II/III)
Statistics
Probability
Applicants with a degree below 2:2 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 overall score of 6.0 with a minimum of 5.5 in all components.
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
Conversion programme which assumes some undergraduate skills as offered in Computer Science, Engineering, Mathematics or Statistics. Designed to be taken by applicants without a sufficient programming and statistics skill set. The programme includes compulsory conversion modules in Programming and text analytics with R and Data analysis and statistics with R. The two conversion modules allow to engage and to deepen the knowledge of applications in data science and to join the major part of our established Master in Data Science.
Developed together with our industrial partners to ensure that employer needs are met. Our partners include BT, Profusion, Essex County Council, Essex Police, and Suffolk County Council. The University of Essex is committed to transformational education and inclusion, focused on learning opportunities for every student, responsive to our students’ needs and aspirations. Reflects this by supporting every student, from every background, and removing the barriers to their education.
The course benefits from an environment with many knowledge transfer partnerships of Innovate UK, which support students through placements and an interdisciplinary outreach culture.
The course introduces
- programming and examples in text analytics.
- Relational databases and SQL are developed and used for relevant applications from humanities, life sciences, linguistics, marketing and social science.
- The course encourages statistical thinking by data visualisations and guides students to develop their creativity within a scientific framework. Compulsory modules include modelling experimental data and machine learning and decision making. Optional modules include Applied Statistics, Statistical Methods, Network Analysis, Information Retrieval, Natural Language Engineering, Dynamic Programming and Reinforcement Learning, Text Analytics, Data Science and Decision Making and Big-Data for Computational Finance.
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: A systematic, extensive and comparative knowledge and understanding of programming and text analytics.
A2: A systematic, extensive and comparative knowledge and understanding of relational databases and SQL.
A3: A systematic, extensive and comparative knowledge and understanding of some statistical modelling methods and techniques, such as sampling schemes, multivariate techniques, regression modelling and stochastic modelling.
A4: A systematic, extensive and comparative knowledge and understanding of
some data analysis methods for analysing statistical models, such as
graphic visualisation, statistical estimation and hypothesis test.
A5: A systematic, extensive and comparative knowledge and understanding ofhow to develop and to apply machine learning methods.
A6: A systematic, extensive and comparative knowledge and understanding of
how to apply computational and statistical techniques into different areas,
such as business, economics, finance, epidemiology and medical research.
Learning methods
Lectures are the main method of delivery for the concepts involved in A1 – A6. 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, laboratories, assignments (A1 – A6).
Assessment methods
Achievement of knowledge outcomes is assessed through open-book tests, marked coursework,
laboratory reports, programming and statistical assignments and project reports
(A1-A6). Regular problem sheets provide formative assessment in all modules.
B: Intellectual and cognitive skills
B1: Identify an appropriate analytical, computational, mathematical and/or statistical model for a specific data-analytical question
B2: Analyse a given data-analytical problem and select the most appropriate computational and statistical tools for its solution
B3: Data pre-processing as part of analytical and statistical data analysis.
B4: Use data science skills 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 – B3 are developed through exercises supported by classes and labs.
B4 is acquired through MA981 dissertation and final projects coursework in MA334, MA304 and MA336.
Assessment methods
Achievement of intellectual/cognitive skills is assessed through open-book tests, marked coursework, laboratory reports, programming and statistical assignments and project reports (B1-B3). B4 by MA981 dissertation and final projects coursework in MA334, MA304 and MA336.
C: Practical skills
C1: Use knowledge, understanding and skills in the systematic and critical assessment of mathematics, programming languages and computational tools and packages.
C2: Use their knowledge, understanding and skills to apply a mathematical,
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: Use knowledge, understanding and skills in preparing a report.
Learning methods
The practical skills of computer science, statistics and operations research are developed in online learning, 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 projects and final projects coursework in MA334, MA304 and MA336.
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; online assignments.
Methods employed to assess practical skills C5-C6 include writing reports.
D: Key skills
D1: Writing data science arguments, ideas, outputs and other information clearly into a report
D2: Use appropriate IT facilities as a tool for developing computer programmes and data analytics and text processing
D3: Use and interpret data science 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
Learning methods
D1 is practised throughout the course in the writing of thesis reports, solutions to data science and applied problems.
D2 is developed through the use of programming languages and computer packages in a number of data science modules.
D3 and D4 are developed and enhanced in all data science modules.
D5 is developed in various data science modules, through exercises and assessments.
D6 is developed and enhanced throughout the course.
Assessment methods
D1 is assessed through coursework and examinatins.
D2 is assessed primarily through coursework.
Assessment of the key skills D3 and D4 is intrinsic to subject based assessment in data science.
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.