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  • Data Analytics

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 able to effectively structure, analyse and gain insight from a wide range of complex data.

The Data Analytics programme is taught by staff with 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, with high demand for qualified data scientists in the 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 2023

Application close date June 2023

Duration 16–24 months

Location Moulsecoomb

Accreditation Statistical Analysis System (SAS)

Submit an application

Ask us about apprenticeships

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.

Statistical Analysis System (SAS) logo

The University of Brighton Mathematics department has Statistical Analysis System (SAS) Academy status

 

Syllabus

Core modules

  • Introduction to 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

Options*

  • Risk Analysis and Retail Finance
  • Multivariate Analysis and Statistical Modelling
  • Medical Statistics
  • Programming for Analytics with SAS
  • Project in Statistical Data Analysis with R
  • 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.

*Option modules are indicative and may change, depending on timetabling and staff availability.

 

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 Data Analytics and Digital MSc and Technology Solutions Specialist (Data Analytics) Masters Degree Apprenticeship since June 2019.

Dr Sónia Timóteo Inácio's full profile

Dr Alexey Chernov

Dr Alexey Chernov is a Senior Lecturer of Mathematical Sciences, in 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

Dr Alexey Chernov

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

Dr Anestis Touloumis

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

The statistics side of my skillset is one that I wanted to hone further and become a more rounded developer, which is why I chose this particular programme. I work full-time, so this apprenticeship afforded me the ability to work and study at the same time because I wouldn't have been able to do that otherwise.

James Lloyd, Solutions Developer, Sussex Partnership NHS Foundation Trust

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

Location

Moulsecoomb campus

Two miles north of Brighton seafront, Moulsecoomb is our largest campus and student village. Over the last four years Moulsecoomb has undergone a major transformation, planned with accessibility, inclusivity and sustainability in mind.

On campus you’ll find professional-standard facilities and learning resources for all of our subjects and a brand new academic building Elm House, alongside the library, student centre, fitness facilities and the Students’ Union.

Moulsecoomb has easy access to buses and trains and to all the exciting things happening in our home city.

New buildings in the Moulsecoomb development viewed from the street

Why choose us?

  • Apprenticeships rated 'Good' by Ofsted
  • 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
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