Page 1 MSc Data Analytics

UNIVERSITY OF BRIGHTON COVID-19 Course Delivery Statement 2020/21

School CEM

Name of Course(s) MSc Data Analytics, Data Analytics Apprenticeship

Are there minimum equipment requirements for students?

Yes. Students require to have/own a computer connected to broadband internet, MS Teams and the recommended software for each module.

Are there minimum hardware requirements for students?

Intel i5 or equivalent or better

Windows 10

8Gb RAM, 256Gb SSD

Full HD screen

Course Specific Delivery Statement: The course, in semester 1, will be delivered through a blend of live sessions (via MS Teams or face-to-face where practicable) within scheduled teaching hours and asynchronous delivery via StudentCentral. Student contact hours remain the same as outlined in the module specification, the balance of scheduled and independent study will not change. Each taught module contact time per week will consist of a single 3-hour event available online. This will include access to online teaching resources as well as interactive sessions, computer practical sessions and guided-independent work with discussion. When possible, taught modules are timetabled in continuous blocks with two modules per day, so full-time students need to plan their taught sessions only twice a week. Supporting materials and online lectures will be available on the Studentcentral area for each module, whenever possible the material will be made available 48 hours in advance to allow for the guided independent work. Modules are mainly assessed by individual coursework assignments; this allows the student to schedule the assignment within their overall workload. There is a group presentation assessment for one module in Semester 1, which will be conducted online (instead of on-campus) through the dedicated MS Teams channel.

Other permanent changes There has been a change to the structure of your course module MM702 'Data Mining and Knowledge Discovery in Data' has changed status from optional to compulsory. Module MM706 'Programming for Analytics has been withdrawn. The title of module MM703 has changed from 'Data Visualisation and Analysis' to 'Introduction to Statistical Data Analysis with R'. The title of module MM704 has changed from 'Risk Analysis and Retail Finance' to 'Forecasting and Credit Risk Analysis'. In addition - a new optional placement module has been added.

PROGRAMME SPECIFICATION Final

PART 1: COURSE SUMMARY INFORMATION

Page 2 MSc Data Analytics

Course summary

Final award MSc Data Analytics Postgraduate Diploma in Data Analytics

Postgraduate Certificate in Data Analytics

Intermediate award Postgraduate Certificate in Data Analytics

Postgraduate Diploma in Data Analytics

Course status Validated

Awarding body University of Brighton

School Computing, Engineering and Mathematics

Location of study/ campus Moulsecoomb

Partner institution(s)

Name of institution Host department Course status

1. N/A SELECT

2. N/A

3. N/A

Admissions

Admissions agency Direct to School

Entry requirements Include any progression opportunities into the course.

Check the University’s website for current entry requirements.

Normally, an honours degree (or equivalent experience) at 2:2 or above in a subject involving substantial quantitative element (e.g. mathematics, statistics, computer science, engineering, economics) or at 2:1 or above in any subject if the applicant can demonstrate some familiarity with and aptitude for mathematical and statistical concepts and methods.

Individual offers may vary.

Applicants may be interviewed by course team members if necessary.

Claims for the accreditation of prior learning will also be considered.

If English is not the applicant's first language: IELTS 6.5 overall with 6.0 in writing, or equivalent.

This programme has been validated to combine either a 12 or 8 week Extended Masters (EMA) English Language pathway route. Programme specifications for the English Language component of the Extended Masters route can be found at: https://www.brighton.ac.uk/international/study-with-us/courses-and-qualifications/brighton-language-institute/eap-programmes/extended-masters/index.aspx

Start date (mmm-yy) Normally September

Sept 20

Mode of study

Mode of study Duration of study (standard) Maximum registration period

Full-time 1 year 6 years

Part-time 2 years 6 years

Sandwich 2 years 6 years

Distance Select Select

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Course codes/categories

UCAS code

Contacts

Course Leader (or Course Development Leader)

Dr Sónia Timóteo Inácio

Admissions Tutor Dr Sónia Timóteo Inácio

Examination and Assessment

External Examiner(s)

Name Place of work Date tenure expires

Dr Penny Holborn Course Leader MSc Applied Data Science & Academic Lead for Welsh Data Science Graduate Programme, University of South Wales

31/12/2024

Examination Board(s) (AEB/CEB)

Postgraduate Computing, Engineering and Mathematical Sciences AEB / CEB

Approval and review

Approval date Review date

Validation May 20141 May 20202

Programme Specification May 20203 May 20214

Professional, Statutory and Regulatory Body 1 (if applicable):

5

Professional, Statutory and Regulatory Body 2 (if applicable):

Professional, Statutory and Regulatory Body 3 (if applicable):

PART 2: COURSE DETAILS

AIMS AND LEARNING OUTCOMES

Aims

The aims of the course are:

The overall aim of the course is to provide students with a unique combination of mathematical and statistical data analytics skills. These skills will develop in tandem with the skills necessary within complex data specific projects, to oversee and manage; critically appraise; assess feasibility; risk analyse; present and see projects through to completion.

1 Date of original validation. 2 Date of most recent periodic review (normally academic year of validation + 5 years). 3 Month and year this version of the programme specification was approved (normally September). 4 Date programme specification will be reviewed (normally approval date + 1 year). If programme specification is applicable to a particular cohort, please state here. 5 Date of most recent review by accrediting/ approving external body.

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This course therefore aims to meet head-on the statistical, computational and business analytical requirements of the burgeoning Data Science industry by producing graduates with well-rounded skills and expertise in specialist data analytics and associated software, quantitative methods and techniques, business intelligence and who are able to assess project viability and manage large data projects successfully. More specifically, the course aims to meet industrial, commercial, academic and public sector requirements for graduate employment in an analytics capacity by

producing graduates with the ability to derive information requirements relating to strategic imperatives, and who have the capacity to recommend analytic approaches suitable for specific data analysis and can design and test analytics solutions using a range of potential software platforms.

equipping students with the mathematical and statistical knowledge specific to a data analytics environment, and enhancing the student’s ability to implement appropriate quantitative analyses to large data projects.

developing the students’ ability to construct, present and defend the business and financial case for the implementation of an analytics project.

furnishing students with, and further developing their decision making and project management skills appropriate to an analytics environment.

Learning outcomes

The outcomes of the main award provide information about how the primary aims are demonstrated by students following the course. These are mapped to external reference points where appropriate6.

Knowledge and theory On successful completion of the course the graduate should be able to:

LO1. Critically evaluate the requirements for the solution to an analytics problem in terms of data structures and technologies, and produce appropriate specifications and solutions.

LO2. Develop a coherent plan for an analytics project and present and defend the associated business case.

LO3. Design, test and implement relevant analyses using appropriate software tools including programming using an appropriate software platform.

LO4. Manage and critically appraise the success of a data analytics project using appropriate project management techniques and software.

LO5. Ensure the ethical and legal use of data collected and analysed according to established professional codes of practice.

Skills Includes intellectual skills (i.e. generic skills relating to academic study, problem solving, evaluation, research etc.) and professional/ practical skills.

LO6. Apply a range of statistical and other analytical skills appropriate to a specified business problem that may derive from an unfamiliar environment.

LO7. Specify, design, implement and test software to meet data specific project goals.

LO8. Critically assess the potential for the application of contemporary techniques not formally taught.

LO9. Function effectively as part of a project team and take responsibility for the management of functional aspects of a project.

LO10. Design and maintain both technical and application documentation to a high standard.

LO11. Evaluate and assess the success of a project and make recommendations for improvement.

LO12. Lead and manage teams applying data analytics for business success.

QAA subject benchmark statement (where applicable)7

Mathematics, statistics and operational research benchmark and annex:

http://www.qaa.ac.uk/Publications/InformationAndGuidance/Pages/Subject-benchmark-statement-Mathematics-statistics-and-operational-research.aspx

6 Please refer to Course Development and Review Handbook or QAA website for details. 7 Please refer to the QAA website for details.

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http://www.qaa.ac.uk/Publications/InformationAndGuidance/Pages/Annex-to-Subject-benchmark-statement-Mathematics-statistics-and-operational-research.aspx

PROFESSIONAL, STATUTORY AND REGULATORY BODIES (where applicable)

Where a course is accredited by a PSRB, full details of how the course meets external requirements, and what students are required to undertake, are included.

N/A

LEARNING AND TEACHING

Learning and teaching methods

This section sets out the primary learning and teaching methods, including total learning hours and any specific requirements in terms of practical/ clinical-based learning. The indicative list of learning and teaching methods includes information on the proportion of the course delivered by each method and details where a particular method relates to a particular element of the course.

Overview

The course learning and teaching methods enable the aims and the learning outcomes to be achieved, taking into consideration the diverse learning styles and needs of a wide range of students’ background knowledge and skills.

Formal teaching will comprise a blend of lectures, tutorials/workshops and computer labs delivered weekly.

The taught modules comprise 20 credits, which indicates that the total learning hours will be 200 hours per module of which approximately 20% will constitute the formally taught element. It is expected that independent study will make up the remainder.

Placement option

Students can opt for a placement module option within the MSc Data Analytics. The placement is expected to start shortly after the taught modules and the start date will be agreed with the employer.

Students will be supported to find a suitable industrial placement, minimum of 24-weeks placement. Full-time students may opt to undertake it between July and January, given that students secure a placement. Students have the opportunity to arrange a longer placement period, up to 38 weeks, between July and May/June. This module aims to enable students to acquire employability skills, to develop analytics skills and to expand their knowledge in a professional environment.

SAS Joint Certification

The MSc in Data Analytics has been developed a collaboration with industry-leading experts in statistics and analytics from the SAS Institute to set up a “SAS Joint Certification”. SAS Certifications are among the most globally recognised credentials in the industry, being of significant value to students as it is one of the most demanded skills for data scientists in the local and international job market.

The SAS joint certificate in Data Analytics require students to complete the optional module “Programming for Analytics with SAS” and using the expertise acquired from the course to perform their final project MM708 module using SAS software.

Additionally, students are offered the option of embarking on the SAS-run Software Certified Young Professionals programme (SCYP) at no cost. This online programme is beyond MSc standard requirements and requires extra hours of SAS training.

Research-informed teaching

The course is delivered by colleagues and guest lecturers who are research active. In addition, links with the industry allow students to be presented with real case scenarios and work experience in the field of statistics in the industry.

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Appropriate research papers are introduced and used alongside the teaching material and added to the reading list in the module area via StudentCentral. This encourage students to use research papers that will test their understanding of how the taught material is applied in real-world situations, which contributes to providing students with the capacity to conduct individual research and to go beyond the taught material.

In the final project module MM708 students are often exposed to the research interests of the academic supervisor(s).

Education for Sustainable Development

There is currently a burgeoning accumulation of data in almost every walk of life, ranging from internet customer behavioural data to medical, economic and financial data as well as vast stockpiles of environmental data. The combination of complex data analytics and business intelligence skills inherent in the MSc Data Analytics provides an ideal and unique opportunity to embed and apply the principles of sustainability within the teaching and learning environment.

With the abundance of available data as noted above, there is ample opportunity for the direct exploration and analysis of large data sets within the context of global sustainability, for example environmental, climate change and health data, as well as population dynamics; an area of much interest, which can be examined in the modules “Multivariate Analysis and Statistical Modelling”, “Medical Statistics” and “Programming for Analytics with SAS”.

Additionally, the sustainability of business and financial environments and models as well as customer base analysis, maintenance and enhancement can be explored in the modules “Business Analytics Strategy & Practice” and “Forecasting and Credit Risk Analysis”.

Sustainable technological development is of vital importance if we are to anticipate and solve problems and issues arising in areas such as those outlined above. Students on the course will develop research skills and skills of critical thinking relevant to the principle of sustainability whilst analysing data sets selected for that purpose.

ASSESSMENT

Assessment methods

This section sets out the summative assessment methods on the course and includes details on where to find further information on the criteria used in assessing coursework. It also provides an assessment matrix which reflects the variety of modes of assessment, and the volume of assessment in the course.

All modules are assessed using the assessment criteria detailed on the individual modules descriptions, which are linked to the learning outcomes for that module. Modules are mainly assessed by individual coursework assignments. This allows the student to schedule the assignment within their overall workload. The MSc Data Analytics course aims at training a data analyst for business environment and hence course assessments are aligned with this aim and include business-style presentations (ISM122, MM701) and written data analysis reports ranging from set exercises to practical problem exploration. The table below shows the minimum set of methods used to assess each course learning outcome.

Learning Outcome Assessment method Module Number of credits

LO1.

Report

Presentation

ISM122

MM701

MM702

MM703

MM705

MM708

MM709

MM711

20

20

20

20

20

60

20

20

LO2. Report ISM122 20

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MM701

MM702

MM703

MM708

20

20

20

60

LO3.

Report

Presentation

MM703

MM704

MM705

MM708

MM709

MM711

20

20

20

60

20

20

LO4. Report

MM701

MM703

MM708

20

20

60

LO5.

Practical assignment,

Report

Presentation

ISM122

MM701

MM705

MM709

MM708

MM799

20

20

20

20

60

0

LO6. Report

Practical assignment

MM703

MM704

MM705

MM708

MM709

MM711

MM799

20

20

20

60

20

20

0

LO7.

Report

Presentation

MM702

MM703

MM708

MM709

MM711

20

20

60

20

20

LO8. Report

MM701

MM708

MM799

20

60

0

LO9. Report

MM701

MM708

MM799

20

60

0

LO10. Report

MM701

MM703

MM708

MM711

MM799

20

20

60

20

0

LO11. Report

MM701

MM703

MM704

MM708

MM799

20

20

20

60

0

LO12. Report MM799 0

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SUPPORT AND INFORMATION

Institutional/ University All students benefit from:

Course Handbook

Extensive library facilities

Computer pool rooms

E-mail address

Welfare service

Personal tutor for advice and guidance

Course-specific Additional support, specifically where courses have non-traditional patterns of delivery (e.g. distance learning and work-based learning) include:

In addition, students on this course benefit from:

Studentcentral: student intranet for the University of Brighton; it is a one-stop-shop for everything students need while studying at the University of Brighton. Studentcentral includes pages for individual schools, modules, the ASK Study Guide and Student Life. In course and module areas students will find handbooks, timetables, course announcements, course material and reading lists.

Staff: Course Leader, Module Leaders, Personal Tutors, Technical support, School Administrative support.

Specialist facilities: Specialised postgraduate computing laboratory and specialist software in multiple computing spaces throughout the School including SAS and SAS Visual Analytics, SPSS, SPSS Modeller, R, RStudio, Minitab, Maple and Matlab.

Research Informed learning

The course itself is specifically designed to nurture students’ learning capabilities through research, and aims to train students as critically reflective researchers and learners. The School is supported by an Industrial Advisory Board (IAB) for Mathematics and Computing. In the final stage of the course students will complete a project under the supervision of a member of teaching staff with in many cases, mentoring contributions from IAB members. It is also envisaged that a number of IAB industry partners will contribute case studies for both the final projects and continuous assessment components.

The IAB comprises a number of Data Analytics specialists including, Tom Khabaza who was founding chairman of the Society of Data Miners and co-author of the data mining software Clementine and methodology CRISP-DM, and was elemental in designing the course.

Specific comments from IAB members include: “In the games industry the use of Data Analytics is becoming more and more important and we would love to be involved with students on final projects by either providing ideas and data for projects or by mentoring students.” Andrew Eades, CEO, Relentless Software.

It is envisaged that many of the projects will arise out of and become part of the research interests of teaching staff.

Examples of MSc Data Analytics Course Team research include:

Dr Sonia Inacio includes in her areas of research Statistical Inference in Linear Models and in the private sector Credit Scoring, Forecasting, Logistics Regression. Example publications are: - Inácio S, Oliveira M. M., Mexia J. T. (2015) Confidence Intervals for large non-centrality parameters, Discussiones Mathematicae Probability and Statistics. - Ferreira D, Ferreira SS, Nunes C, Inácio S (2013) Inducing pivot variables and non-centrality parameters in elliptical distributions. AIP Conf. Proc. 1558: 833 (http://dx.doi.org/10.1063/1.4825625)

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Dr Anestis Touloumis’s area of research is medical statistics includes in his portfolio of publications: - Touloumis, A., Tavaré, S. and Marioni, J.C. (2015). Testing the Mean Matrix in

High-Dimensional Transposable Data. Biometrics 71, pp: 157-166. - Piccirillo, S.G.M., Spiteri, I., Sottoriva, A., Touloumis, A., Ber, S., Heywood, R., Francis, N.J., Howarth, K.D., Collins, V.P., Venkitaraman, A.R., Curtis, C., Marioni, J.C., Tavaré, S., Watts, C. (2015). Contributions to Drug Resistance in Glioblastoma Derived from Malignant Cells in the Sub-ependymal Zone. Cancer Research 75, pp: 194-202.

Dr Alexey Chernov conducts research in Machine Learning. A recent publication is: - Adamskiy, D., Koolen, W., Chernov, A., Vovk, V. A closer look at adaptive regret. Journal of Machine Learning Research, 17(23):1-21, 2016.

PART 3: COURSE SPECIFIC REGULATIONS

COURSE STRUCTURE

This section includes an outline of the structure of the programme, including stages of study and progression points. Course Leaders may choose to include a structure diagram here.

The programme structure requires full time students to study three modules in Semester 1 and three modules in Semester 2 totalling 60 credits per semester. For a part-time mode please see the example of which modules to study in each semester included after the course structure diagram below.

For all students, Semester 1 modules are compulsory and Semester 2 one module is compulsory and students choose two more modules from four optional modules.

Full-time students can opt to undertake the 24 weeks industrial placement module between July and January, completing their final project between February and April. There is an option for a longer industrial placement, up to 38 weeks, and those students are normally expected to finish it in time to submit their project in October.

The university will assist in finding a placement but cannot guarantee it.

Full-time students in the MSc Data Analytics are expected to undertake their project under supervision in semester 3, between July and September, to submit in October.

Course Structure Diagram (compulsory modules are in Bold):

Semester 1 Semester 2

ISM122

Data Management (C)

MM702

Data Mining & KDD

(C)

MM701

Business Analytics Strategy & Practice

(C)

MM704

Forecasting and Credit Risk Analysis

(O)

MM703

Introduction to Statistical Data Analysis with R

(C)

MM705

Multivariate Analysis and Statistical Modelling

(O)

MM709

Medical Statistics

(O)

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MM711

Programming for Analytics with SAS

(O)

Semester 2: 40 credits are chosen from the optional modules.

Semester 3: students undertake their final project module (MM708);

OR

start an industrial placement, MM799 (this module carries zero credits).

An example of the course structure in a part-time mode: Year 1 Semester 1: MM703 AND {MM701 OR ISM122}

Semester 2: MM702 AND 1 optional module

Year 2 Semester 3: MM701 OR ISM122

Semester 4: 1 optional module

Semester 5: MM708

Modules

Status:

M = Mandatory (modules which must be taken and passed to be eligible for the award)

C = Compulsory (modules which must be taken to be eligible for the award)

O = Optional (optional modules)*

A = Additional (modules which must be taken to be eligible for an award accredited by a professional, statutory or regulatory body, including any non-credit bearing modules)

* Optional modules listed are indicative only and may be subject to change, depending on timetabling and staff availability

The University reserves the right to withdraw and/or replace modules.

Level8

Module code

Status Module title Credit

7 ISM122 C Data Management 20

7 MM703 C Introduction to Statistical Data Analysis with R 20

7 MM701 C Business Analytics Strategy and Practice 20

7 MM705 O Multivariate Analysis and Statistical Modelling 20

7 MM702 C Data Mining & KDD 20

7 MM704 O Forecasting and Credit Risk Analysis 20

7 MM709 O Medical Statistics 20

7 MM708 M Project 60

7 MM711 O Programming for Analytics with SAS 20

7 MM799 O Data Analytics Industrial Placement 0

Specialist Resources:

It is anticipated that industry specialists will contribute to course delivery.

8 All modules have learning outcomes commensurate with the FHEQ levels 0, 4, 5, 6, 7 and 8. List the level which corresponds with the learning outcomes of each module.

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Specialist software will be provided to facilitate delivery. Examples will include SAS, R and SPSS Modeller.

Sample data sets will be created in collaboration with industrial advisors, to illustrate the application of analytics techniques on realistic large data sets.

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AWARD AND CLASSIFICATION

Award type Award* Title Level Eligibility for award Classification of award

Total credits9 Minimum credits10 Ratio of marks11: Class of award

Final MSc Data Analytics 7 Total credit 180 Minimum credit at level of award Other: 150

Level 7 marks Postgraduate degree

**Final/Intermediate

PG Dip Data Analytics 7 Total credit 120 Minimum credit at level of award 90

Level 7 marks Not applicable

**Final/Intermediate

PG Cert Data Analytics 7 Total credit 60 Minimum credit at level of award 40

Level 7 marks Not applicable

Select Select Total credit Select Minimum credit at level of award Select

Select Select

*Foundation degrees only

Progression routes from award:

Award classifications Mark/ band % Foundation degree Honours degree Postgraduate12 degree (excludes PGCE and BM BS)

70% - 100% Distinction First (1) Distinction

60% - 69.99% Merit Upper second (2:1) Merit

50% - 59.99% Pass

Lower second (2:2) Pass

40% - 49.99% Third (3)

**Final/Intermediate award dependent upon entry route

9 Total number of credits required to be eligible for the award. 10 Minimum number of credits required, at level of award, to be eligible for the award. 11 Algorithm used to determine the classification of the final award (all marks are credit-weighted). For a Masters degree, the mark for the final element (e.g, dissertation) must be in the corresponding class of award. 12 Refers to taught provision: PG Cert, PG Dip, Masters.

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EXAMINATION AND ASSESSMENT REGULATIONS

Please refer to the Course Approval and Review Handbook when completing this section.

The examination and assessment regulations for the course should be in accordance with the University’s General Examination and Assessment Regulations for Taught Courses (available from staffcentral or studentcentral).

Specific regulations which materially affect assessment, progression and award on the course e.g. Where referrals or repeat of modules are not permitted in line with the University’s General Examination and Assessment Regulations for Taught Courses.

Exceptions required by PSRB These require the approval of the Chair of the Academic Board