(MSc) Master of Science
Applied Data Science
Current
University of Essex
University of Essex
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Masters
Full-time
MSC G305JS
10/05/2023
Details
Professional accreditation
None
Admission criteria
A 2:2 degree, or equivalent, in any discipline.
Applicants with a strong background in Science, Technology, Engineering & Mathematics (STEM) may be considered for the MSc Data Science and It's Applications.
We encourage you to consider this as a choice if you come from a STEM background.
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 does not build on undergraduate skills as offered in Computer Science, Engineering, Mathematics or Statistics, but adds to undergraduate degrees in the Humanities, Social or Life Sciences postgraduate skills in essential data science methods with various applications. Designed to be taken by applicants with backgrounds especially in humanities, social sciences, life sciences and business studies. The programme covers case studies and applications of AI and data science from humanities, social sciences, life sciences and business studies. Develops data science by enhancing skills and applications from these subjects.
Developed together with our industrial partners to ensure that employer needs are met. 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:
- A foundational maths knowledge for levelling up general maths skills and ensuring the rest of delivered materials in the course are built on solid grounds.
- Programming with the R language and examples in text analytics.
- Basic statistics and decision making is taught alongside with its applications using programming languages.
- 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.
- Using R statistical modelling and decision making is taught.
- Linear and generalised linear models are used for experimental and observational data.
- Understanding of artificial intelligence, deep and statistical learning machine learning and deep learning is at the centre of the course.
- Optional Other relevant modules include network analysis applied statistics, information retrieval, digital economy, and survey sampling.
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 with the R language
A2: A systematic, extensive and comparative knowledge and understanding of relational databases and SQL.
A3: A systematic, extensive and comparative knowledge and understanding of statistical thinking by data visualisations and of developing creativity within a scientific framework
A4: A systematic, extensive and comparative knowledge and understanding of statistical modelling and decision making
A5: A systematic, extensive and comparative knowledge and understanding of linear and generalised linear models for experimental and observational data
A6: A systematic, extensive and comparative knowledge and understanding of artificial intelligence, deep and statistical learning
A7: A systematic, extensive and comparative knowledge and understanding of basic and core mathematical concepts relevant to Data Science.
Learning methods
Lectures are the main method of delivery for the concepts involved in A1 – A7. 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 – A7).
Assessment methods
Achievement of knowledge outcomes is assessed through open-book tests, marked coursework, laboratory reports, programming and statistical assignments, project reports and oral examinations (A1-A7). 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 individual modules.
Assessment methods
Achievement of intellectual/cognitive skills is assessed through open-book tests, marked coursework, laboratory reports, programming and statistical assignments, project reports and oral examinations (B1-B3). B4 by MA981 dissertation and final projects coursework in individual modules.
C: Practical skills
C1: Use their knowledge, understanding and skills in the systematic and critical assessment of programming languages and computational tools and packages
C2: Use their knowledge, understanding and skills to apply a rigorous, analytic, highly quantitative approach to a problem
C3: Use their knowledge, understanding and skills in managing, organising and presenting data
C4: Use their knowledge, understanding and skills in gathering and processing information from different sources
C5: Use their knowledge, understanding and skills in searching literature
C6: Use their knowledge, understanding and skills in preparing a report
C7: Use their knowledge, understanding and skills in giving presentation
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-C7 are acquired and enhanced through MA981 projects and final projects coursework in individual modules.
Assessment methods
Achievement of practical skills C1 and C2 is assessed through marked coursework, project reports and oral examinations.
Methods employed to assess practical skills C3 and C4 typically include: class presentations; written coursework; written exams; class tests; online assignments.
Methods employed to assess practical skills C5-C7 include: writing thesis reports and thesis viva.
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 viva, coursework and oral examinations.
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.