Digital and Technology Solutions Specialist (Data Analytics) Masters Degree Apprenticeship

Our Digital and Technology Solutions Specialist (Data Analytics) Masters Degree Apprenticeship was developed in collaboration with data-driven organisations including Statistical Analysis System (SAS). The programme enables apprentices to become data specialists with the ability to effectively structure, analyse and gain insight from a wide range of complex data.

The Data Analytics programme is taught by staff who have expertise in areas including sequential forecasting, natural language processing and image processing. Apprentices’ learning is informed by this research and by current business developments through case studies looking at real-world data sets, research questions and scenarios.

Apprentices gain advanced skills in specialist data analytics and associated software including SAS, SPSS Statistics, SPSS Modeller, R, RStudio, Minitab, Maple and Matlab. They will also develop quantitative methods and techniques, business intelligence and communication skills.

SAS Certifications are among the most globally recognised credentials in the industry, being of significant value to apprentices as it is one of the most demanded skills for data scientists in the local and international job market.

This programme is aimed at applicants with a non-specialist first degree wishing to obtain or top-up skills in data analysis.

Make sure that you’ve read the FAQs, and that you meet the eligibility criteria, before you submit an application.

key information

University of Brighton award Data Analytics MSc

Apprenticeship Standard Digital and Technology Solutions Specialist

Level 7

Start Date September 2021

Duration 16-24 months

Location Moulsecoomb

Accreditation Statistical Analysis System (SAS)

Further information and enquiries

submit an application

Programme details

Programme structure

The programme is designed to cover three main areas

Data management – structuring and manipulating data for analysis purposes
Data interpretation – methods of machine learning and statistical analysis using industry-standard software such as R, SAS and Python;
Project management – business-specific and strategic aspects of analytics

With support from the employer, apprentices are required to spend 20% of paid working hours on studies and be able to evidence this. This is a statutory requirement for apprenticeships and the funding is conditional on this being met. Off-the-job learning includes activities which have not previously been a part of the role or furthers apprentices development into new areas.

Entry requirements

A 2:1 or higher degree in any subject and some knowledge and skills in data analysis, statistics and programming
A 2:2 or higher degree in a relevant subject such as Mathematics, Statistics, Data Mining or Artificial Intelligence
Level 2 qualifications in English and Maths
IELTS 6.5 overall with 6.0 in writing

Modules

Core

Introduction to Statistical Data Analysis with R / Project in Statistical Data Analysis with R
Business Analytics Strategy and Practice
Data Management
Data Mining and Knowledge Discovery in Data
Final Apprenticeship Project in Data Analytics

Optional modules

Risk Analysis and Retail Finance
Multivariate Analysis and Statistical Modelling
Medical Statistics
Programming for Analytics with SAS
Machine Learning and Artificial Intelligence
The Analysis of Time Series

The programme has built-in flexibility and can be adapted to the needs of the apprentices background.

Core skills

Upon completion of the programme, apprentices will be equipped to:

apply a range of statistical and analytical skills appropriate to a specified business problem
critically evaluate the requirements for the solution to an analytics problem in terms of data structures and technologies, and produce appropriate solutions
specify, design, implement and test software to meet data-specific project goals using appropriate tools including programming
manage and critically appraise the success of a data analytics project using appropriate project management techniques and software

function effectively as part of a project team and take responsibility for the management of aspects of a project
ensure the ethical and legal use of data collected and analysed according to professional codes of practice
design and maintain both technical and application documentation to a high standard
evaluate and assess the success of a project and make recommendations for improvement.

Teaching and assessment

The course itself is specifically designed to nurture apprentices’ learning capabilities through research, and aims to train apprentices as critically reflective researchers and learners. The School is supported by an Industrial Advisory Board (IAB) for Mathematics and Computing.

Teaching is comprised of a blend of lectures, tutorials, workshops and computer labs. Total self-led learning and teaching hours per 20-credit module equals to 200 hours.

Additionally, apprentices will have an academic advisor overseeing their workplace-based activities and assessing. This will be achieved through quarterly reviews.

Throughout the programme of study apprentices will be assessed through:

reports
presentations
practical assignments.

This programme of study is an integrated Degree Apprenticeship and includes the End Point Assessment (EPA) which is assessed and graded by an assessor from an awarding university. The EPA comprises a project report and a professional discussion and must be completed over a maximum period of three months after the apprentice has met the gateway requirement.

Employer and apprentice commitments

In order to deliver the programme, the employer undertakes to:

conduct a workplace induction and set out a contract of employment
provide an appropriately qualified workplace mentor to support the apprentice
attend apprentice's progress review (twice a year)
release apprentice for scheduled off-the-job training
have an Apprenticeship Agreement in place from the start of the programme.

Throughout the programme, apprentices will:

attend the workplace for the agreed pattern as set out in the employment contract
record all training sessions and off-the-job learning
attend scheduled university learning sessions and complete all assessments as required by the university and End Point Assessment Organisation
behave in a safe and responsible manner at the workplace and university
engage with regular progress reviews
complete, throughout the apprenticeship, a professional development portfolio.

Staff profiles

Dr Sónia Timóteo Inácio, Course Leader

Dr Sónia Timóteo Inácio is a Senior Lecturer of Mathematical Sciences, in the School of Architecture, Technology and Engineering. She has been the course leader for the MSc Data Analytics and Digital and Technology Solutions Specialist (Data Analytics) Masters Degree Apprenticeship since June 2019.

Dr Sónia Timóteo Inácio's full profile

Dr Alexey Chernov (Course Leader 2016 – 2019)

Dr Alexey Chernov, is a Senior Lecturer of Mathematical Sciences, n the School of Architecture, Technology and Engineering.

Being a mathematician by education, Dr Chernov researches mathematical foundations of machine learning and artificial intelligence and teaches a wide range of mathematical and statistical courses.

He also collaborates with academic and non-academic partners on data analysis applied to practical problems. Dr Chernov is a member of Centre for Secure, Intelligent and Usable Systems, Mathematics, Statistics and Operations Group and Applied Data Analytics Research and Enterprise Group at the University of Brighton.

Dr Alexey Chernov's full profile

Professor Anya Belz

Professor Anya Belz researches in natural language processing, specialising in language generation, evaluation, and most recently image description and text analytics for pharmacovigilance (drug safety).

She is the head of the Applied Data Analytics Research and Enterprise Group, which develops methods for turning the vast quantities of data we now generate daily into information, and ultimately knowledge, that can be used to help people lead healthier lives at all ages, enrich user experiences, and add value to businesses, and optimise manufacturing processes.

Professor Anya Belz's full profile

Dr Anestis Touloumis, Principal Lecturer

Dr Anestis Touloumis's research focuses on the development of novel statistical methods and R software. Dr Touluumis is part of the Centre for Secure, Intelligent and Useable Systems, and the Mathematical Sciences Research and Enterprise Group.

His work is often motivated from multidisciplinary collaborations, mainly with scientists from medical, biological, and computer science disciplines. In particular, he applied his methods of glioblastoma studies and empirical studies in diagrammatic logic.

Dr Anestis Touloumis's full profile

Fees and funding information for apprentices

Course fees

As an apprentice the cost of your learning programme is covered by your employer through their Digital Apprenticeship Service (DAS) account.

Student finance

Higher and degree level apprentices are not eligible for any element of student finance. This is because they receive a salary instead of the student loan package. However, if you are experiencing any financial difficulties, please contact the Student Advice Service on 01273 642 888.

Learning support

Apprentices studying at Higher and degree level may be able to access funding in relation to additional support but this is available through the university and not Disabled Students Allowance. Apprentices may also be able to apply for Access to Work funding for work-based equipment and software. Please contact the Disability and Dyslexia team on 01273 643 799.

Living costs and benefits

Higher and degree level apprentices must fulfil the standard eligibility criteria in order to apply for means tested benefits such as Universal Credit, Child Tax Credit and Housing Benefit. If you have any questions about entitlement to benefits, please contact the Student Advice Service on 01273 643 888.

Higher and degree level apprentices who are undertaking a full-time mode of study can be treated as being exempt from paying Council Tax. Although some programmes require study on a day-release arrangement, this can still be classed as full-time study. You will need to obtain a letter from the Student Information Desk at any campus and send this to your Council Tax department when you enrol on the programme.

Other

Brighton and Hove bus discount

NUS card

How this programme is delivered

Our approach
We want you to spend as much time on campus as possible. From semester 1 of the academic year which starts in September, every student will have some face-to-face, on-campus learning in their timetable. Your timetable is being designed to make sure that you can make the most out of your time on campus, and all of the usual campus facilities – libraries, learning spaces, restaurants, gyms and more – will be open. Your timetable will be available in My Studies from late September.

Teaching on your course
In semester 1 all of your timetabled learning takes place on campus. This includes lectures, seminars and tutorials.

COVID-19
Like all universities we will be following government guidance and will be monitoring the pandemic very closely with our local Director of Public Health. Should a worsening of the pandemic occur and Public Health advice require us to make changes, we have robust plans in place for students to continue their studies remotely and for additional safety measures to be introduced on campus.

Why choose us?

Apprenticeships rated 'Good' by Ofsted
The University of Brighton Mathematics department has Statistical Analysis System (SAS) Academy status
Up-to-date, business relevant training is developed in partnership with our Industrial Advisory Board (comprised of external specialists with substantial industrial experience in data analytics and computing)
Our programmes are delivered by experts who conduct internationally recognised research as well as undertaking regular consultancy work for industry