BSc (Hons) Data Science (Work Based)


Institution code	H36
School of study	School of Physics, Engineering and Computer Science
Course length	Full Time, 3 Years
Location	University of Hertfordshire, Hatfield

Entry requirements
UCAS points: 120-128
A level: BBB-ABB with a minimum of grade B in maths
BTEC: DDM – Applied Science AND A level Maths grade C
Access Course Tariff: Diploma with 45 Level 3 credits of which 15 must be in maths at a minimum of Merit
IB Requirement: IB 120-128 points must include maths at HL grade 4 or above
The University of Hertfordshire is committed to welcoming apprentices with a wide range of qualifications and levels of experience to the Data Science Apprenticeship. We have a flexible approach to admissions and each application will be considered on an individual basis. Individuals with a minimum of two years’ work experience will be considered on an individual basis. A full Initial Needs Assessment (INA) will be carried out at the start of the apprenticeship to identify and discuss prior knowledge.
Additional requirements
GCSE: Grade 4/C in English Language and 4/C in Mathematics.
Employment: In order to be eligible for an apprenticeship, an applicant must:
be nominated by their employer and have a permanent or fixed-term contract for 30 hours per week or more, that is long enough for apprentices to successfully complete the apprenticeship
be employed in a role that provides them with exposure to a range of relevant business areas. This will ensure that apprentices can complete all elements as required by the standard
be able to spend at least 20% of their normal working hours (six hours per week) for off-the-job training over the planned duration of the apprenticeship practical period. (See the ESFA Apprenticeship Funding Rules 2024-25.)
All students from non-majority English speaking countries require proof of English language proficiency, equivalent to an overall IELTS score of 6.0 with a minimum of 5.5 in each band.
If you do not have the required IELTS or equivalent for direct entry on to your degree programme, our Pre-sessional English and International Foundation courses can help you to achieve this level.
For more details on the University of Hertfordshire's entry requirements, please visit our Undergraduate Entry Requirements page.

About the course

Final award: BSc Honours Data Science (work-based)

Our Data Scientist Degree Apprenticeship has been developed by the University of Hertfordshire in partnership with leading employers and subject experts to meet growing global demand for data science talent and prepare apprentices for successful careers as data scientists.

Apprentices will develop the skills and expertise to interrogate data, systems and approaches to unlock insights, improve organisational processes and inform decision making.

Why choose this course?

Our degree apprenticeship programme offers a tailored, work-based learning experience that equips apprentices with the skills and expertise to effectively manage and analyse large datasets within your organisations. The programme empowers apprentices to bridge the gap between theory and practice, enabling you to contribute effectively to your organisation's data-driven decision-making processes and drive meaningful business outcomes.

Apprentices will undergo comprehensive training in specific data analysis techniques and data science problem-solving skills, including statistics, mathematical modelling, algorithm design, and computer programming. You will also develop essential business skills, such as presentation and communication, teamwork, and workload management, ensuring apprentices graduate as well-rounded professionals capable of addressing diverse challenges in the workplace.

One of the unique features of our programme is the emphasis on practical application. Throughout the apprenticeship, apprentices are encouraged to identify and explore opportunities, data, and business questions within your organisation, where you can apply the techniques learned. By integrating real-world work experiences into your projects, the programme enables you to contextualise your learning, making it more relevant and impactful.

What will I study?

The course will develop the knowledge and skills required to collect, organise and study data to provide new business insight. Apprentices will develop an understanding of the context of data science, the ethical, legal and social issues relating to data science, and their implication on your work and organisation.

The course will consist of flexible learning, including a mix of lectures, tutorials, discussions, and group work, as well as directed independent study.

Level 4

Module	Credits	Compulsory/optional
Data Science Programming	30 Credits	Compulsory
In this module, you will learn the fundamentals of programming. Key concepts, such as variable types, functions, loops, and conditionals will be introduced, and core Python packages for data science and mathematical programming will be used, such as numpy, pandas, and scipy. You will also learn how to develop basic mathematical models computationally (e.g. data fitting), implement good-practice in software development following PEP-8 code conventions, and utilise online repositories such as GitHub.
Data Science Principles and Maths	30 Credits	Compulsory
The module is designed to provide apprentices with the principles of planning and conducting data science research in applied contexts. By the end of the module, apprentices will be confident with the language and conventions of data science, including types of data, data structures, and the necessary steps to perform the data handling required for a data science project. They will also understand the ethical and legal aspects and will become comfortable with the basic mathematics underpinning data science, such as vectors, matrices, and simple calculus.
Data Science Computation & Statistics	30 Credits	Compulsory
The module is designed to provide apprentices with basic literacy in the use of computers and statistics. This will include gaining confidence in running different operating systems and developing a broad understanding of how computers store data and can be programmed in different ways. Apprentices will also become familiar with key statistical tools such as Bayes' Theorem, linear regression, chi-squared testing, and a statistical software package, all of which are essential for any Data Scientist.
Data Handling & Presentation	30 Credits	Compulsory
In this module, you will learn more advanced methods for handling numerical data in Python with packages such as Numpy and Pandas. We will then move on to visualising that data using packages such as Matplotlib and Seaborn, as well alternative, non-python visualisation software. You will discover the best methods for presenting data graphically and non-graphically to a range of audiences, and use plots to illustrate complex results in a clear and comprehensive way. The creation of professional-looking reports using the markup language, LaTeX, will also be presented.

Level 5

Module	Credits	Compulsory/optional
Machine Learning for Data Scientists	30 Credits	Compulsory
This module provides knowledge on machine learning and artificial intelligence. It gives a broad but thorough overview of a range of modern methods and techniques used in machine learning with a particular emphasis on how to implement these techniques using an appropriate programming language. Apprentices will learn about the underlying theory and fundamental mathematical/statistical principles, and get hands-on experience of creating, training, evaluating, and using various machine learning techniques, including deep neural networks.
Big Data and Cloud Computing	30 Credits	Compulsory
In this module you will learn how different types of large datasets are stored and represented, including cloud-computing, and how you can use high-performance computing facilities to analyse them and extract key information. You will also learn how to scale up a system and develop code into production quality and how to determine value-for-money for organisations. You will have access to the University's high-performance computing facility and will use it and other work-based computing facilities to solve big data challenges during practical classes and independent study time. You will discover how to pose questions specifically related to large datasets and cloud computing and learn how to implement them computationally. Towards the end of the module, you will carry out a small independent project making use of high-performance computing facilities. You will be encouraged to seek out questions related to your work.
Information Theory and Data Security	30 Credits	Compulsory
This module introduces the apprentice to the analysis of complex data using statistical modelling and information theory. It introduces fundamental concepts such as information, codes, complexity, entropy, and noisy data, and examines their applications in areas such as data compression, encoding and communications. Data security is introduced covering basic cryptography and the use of blockchains (as open distributed ledgers). Apprentices will create a blockchain with simple Python codes.
Professional Teamwork for Data Scientists	30 Credits	Compulsory
The module will consist of a group project with other apprentices and an individual project that is undertaken in the workplace. The group project is to develop a dashboard. The group will participate in a professional discussion about the project informed by the dashboard. The group will use agile working practises to develop the code. An individual project will require the apprentice to identify a real-world data analysis problem within their organisation and discuss the requirements with personnel across different functions in the organisation. The aim is to engage positive working practises and to gain an understanding of how to collect organisational requirements. The student will make decisions based on the requirements collected and will develop suitable data analysis and system recommendations with cost estimates.

Level 6

Module	Credits	Compulsory/optional
Advanced Data Science Analysis	30 Credits	Compulsory
This module explores fundamental techniques in current automated analysis techniques including computer vision and natural language processing (NLP). Practical-based tutorial sessions will help develop proficiency in developing and applying computer vision and NLP algorithms to real data. It will build on concepts already developed in the field of machine learning and AI (e.g. neural networks) and apply them to topics such as image recognition, object classification, speech segmentation and time series analysis.
Data Science Professional Project	30 Credits	Compulsory
In this module apprentices will conduct a substantial research project that is related to their work. The project will tackle a particular topic in data science, where apprentices will deploy their full range of skills to reach a level of depth of understanding and specialised knowledge not possible in individual modules. This will be an opportunity for apprentices to develop independence. Apprentices will be expected to take 'ownership' of their projects and bring their own enthusiasm and creativity to bear. Apprentices will be required to identify their own projects based on a real-world problem within their work environment for which a data analysis solution is required and are expected to demonstrate the full implementation of that solution in the project. Apprentices are also expected to demonstrate an awareness of how to manage such projects and an appreciation of the wider business context within which they would take place.
Data Scientist End Point Assessment	60 Credits	Compulsory
In this module apprentices will prepare for, and undertake, the End Point Assessments for the Data Scientist Integrated Degree. Apprentices will be required to write and submit the final report for the assessment. Apprentices will prepare and practice for the professional discussion that will be informed by the portfolio that the apprentice has prepared during the programme. Apprentices will prepare and practise for the knowledge test of the End Point Assessment. The module will also include career planning for when the apprentice has completed the degree. This module can only be taken once the apprentice has met all the Gateway requirements for the Data Scientist Integrated Degree.

Careers

After successfully completing the degree apprenticeship programme, you will have developed the knowledge, skills and behaviours to work in industry as a data scientist.

Further career opportunities include the following roles:

Data scientist
Data analyst
Data engineer
Data architect
Technology professional.

The University’s careers service provides a range of support to help apprentices develop skills attractive to employers. Visit our careers pages for more information.

Course fact sheets
BSc Honours Data Science (Work Based)	Download

Programme specifications
BSc Honours Data Science (Work Based)	Download

Additional information
Sandwich placement or study abroad year	n/a
Applications open to international and EU students	No

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