Data Scientist Degree Apprenticeship BSc (Hons)

Part-time, work-based undergraduate (4 years)

Blended learning, Cambridge

September

This course is delivered through blended learning: work-based study, online through a combination of our Learning Management System, Canvas and Cambridge Spark’s K.A.T.E.® platform, plus immersive teaching weeks and a hackathon-style bootcamp on our Cambridge campus.

Overview

Develop the skills and expertise to interrogate data, systems and methodologies to unlock insights which help address complex problems, improve organisational processes and inform decision making within your organisation. Gain a BSc (Hons) Data Science degree while you work, with fees funded by your employer and the Government.

Full description
We depend on data science, whether that’s using deep learning to train an algorithm, or helping our warehouse staff plan for peak demand. I look forward to welcoming and working with our first apprentices.
Nick Raikes
Assistant Director of Research, Cambridge Assessment

Careers

There is a recognised significant skills gap in data science based systems specialists in the industry, nationally and internationally. This is despite data scientist roles growing over 650% since 2012, with machine learning engineers, data scientists, and big data engineers ranking among the top emerging jobs*.

Employers have highlighted the importance of data science and its potential to revolutionise a number of industries, from social sciences, physics and engineering to market analysis and banking, while creating significant employment opportunities for data analysts, machine learning specialists and data specialists.

*U.S. Bureau of Labor Statistics.

Modules & assessment

Year one, core modules

  • Introduction to Data Science and Programming
    This module is designed to provide a sound knowledge of the context of data science in relation to computing, software engineering and applications. The wide variety of different areas of study in contemporary data science will be introduced and the diversity of different roles undertaken by data scientists within an organisation will be described. The key role of data management systems will be covered in detail. The ethical and legal issues that underpin work in data science will be explored, together with an understanding of the provisional nature of science.
  • Computer and Network Technology
    This module investigates the components and operation of modern computer systems. It introduces you to the hardware components that enable a computer to process data and the devices that enable data to be input, output and stored. The installation, operation and functionality of modern operating systems and systems software will also be investigated including the operation and configuration of common servers. The fundamentals of networks will be covered as modern computer systems rarely operate in a standalone manner.
  • Workplace Skills and Learning
    Entering the work place you need specific study skills to enable you to maximize your learning potential and take advantage of opportunities available both in the academic setting and the workplace. The work environment provides a rich setting for exploration and discovery of a range of knowledge, skills and understanding. The module is intended to be both preparatory and supportive building a strong foundation for learning and later development.

Year two, core modules

  • Enterprise Analytics
    This module is essential to develop a solid background and confidence in mathematical techniques and analysis, to enable study on a degree programme in technology or engineering studies. The module will help you to critically analyse the analytics of the business domain.
  • Computational Concepts and Algorithms for Data Science
    This module builds on the foundation of basic coding established in Introduction to Data Science and Programming and is designed to introduce you to techniques used in the development of software that are reliable, efficient, useful, and usable. You will gain practical experience developing a software solution to a specific problem.
  • Mathematics and Statistics
    This module provides a sound basis in mathematics, statistics and analysis. You will develop a working knowledge of R, and will learn numerical data types. You will be introduced to probability theory and how it is used to generate models and introduced to data classification from simple clustering to introductory machine learning approaches.
  • Software Tools and Programming for Data Science
    This module is designed to build on the foundations laid in Computational Concepts and Algorithms in Data Science and in Data Engineering to expand your ability to use existing data science tools and frameworks to design and implement pipelines to solve data science problems. The second part of this module looks at dealing with data mining, and applying the latest machine learning approaches to data science problems previously implemented with standard pipelines.
  • Data Engineering
    Data engineering is a process to design, build and manage the information or 'big data' infrastructure. It gives an understanding of how to develop the architecture that helps analyse and process data in the way the organisation needs it. This module will examine the entire data life cycle, including data creation, modelling, representation, analysis, maintenance and disposal.
  • Data Application Programming
    This module will equip you with the understanding of the big data landscape. Big data is about understanding, sorting and extracting knowledge from large volumes of data. Concepts such as Volume, Velocity, Variety will be presented and the Hadoop framework, used for storing and processing Big Data in a distributed manner, will be introduced.

Year three, core modules

  • Principles of Data Science
    In this module the core principles fundamental to data scientists we be examined, and data science will be positioned in the context of other approaches to knowledge discovery (e.g. empirical, theoretical and computational). The core skills required of a data scientist will be developed, including those relating to scientific inquiry, statistics, substantive expertise, and computer programming. The most common data analytic tools and techniques will be introduced, including the use of specialised software for the analysis of large-scale datasets to answer domain-relevant questions.
  • Principles of Artificial Intelligence and Machine Learning
    This module introduces the key concepts of artificial intelligence and machine learning techniques that enable a system to learn from data rather than through explicit programming. These are leveraging data assets and, thus, are becoming essential in the innovation of business operation and in generating more efficient workflows. You will learn about fundamental machine learning techniques and gain practice implementing them and getting them to work.
  • Machine Learning and Data Engineering Bootcamp
    The purpose of the bootcamp is to reflect what it would be like to work as a real data scientist ‘in the job’ in the context of team collaboration and the data science tools. You will learn how to apply fundamental machine learning and data engineering techniques alongside gaining practice in implementing them and getting them to work. The module builds up on previous knowledge gained in the course on key concepts of machine learning and data engineering techniques that enable a system to be trained to learn from data rather than through explicit programming. The module aims to implement and test such concepts through real life scenarios as part of practical projects.
  • Time Series Analysis
    This module will provide a basic introduction to modern time series analysis. Examples of time series are financial market and stock prices, weather data, and many more.

Year four, core modules

  • Data Visualisation
    Data visualisation involves expressing and communicating the findings of data scientists and analysts effectively through graphical means. When coupled with AI software this forms the relatively new specialist area of visual analytics.
  • Deep Learning Techniques and Natural Language Processing
    Natural language processing (NLP) is one of the most important technologies of the modern age. It is one of the crucial parts of artificial intelligence since people use natural language everywhere, eg web search, reviews, emails and ads. This module will equip you with a detailed understanding of the core principles and applications of deep learning to natural language processing.
  • Apprenticeship Final Project (including EPA)
    The individual Apprenticeship Final Project module allows you to engage in a substantial piece of product development work, focused on a topic relevant to their award discipline and apprenticeship specialism selected.

Assessment

The End Point Assessment is made up of three assessment areas.

  1. Knowledge test.
  2. Report (based on a work-based project).
  3. Professional discussion (informed by a portfolio).

Where you'll study

Where can I study?

Blended learning
Person using laptop

Study at a time that suits you, using our learning management system.

More about blended learning

Cambridge
Lord Ashcroft Building on our Cambridge campus

Our campus is close to the centre of Cambridge, often described as the perfect student city.

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Fees & funding

Course fees

Degree apprenticeships are funded by your employer and the Government

£0

Entry requirements

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Main

You will need:

  • Full-time employment in a relevant role
  • 96 UCAS tariff points
  • 3 A levels at grade CCC with at least one STEM (Science, Technology, Engineering, Maths) subject. Maths desirable
  • In addition applicants will need to have 5 GCSEs* at grades C / 4 or above (or equivalent) including English and Maths
  • If English is not a first language, you'll be expected to demonstrate a certificated list of proficiency of at least IELTS 6.0 or above
  • You’ll also need a suitable computer with internet connection, together with sufficient IT competence to make effective use of word processing, internet and email.

If you don’t meet the above requirements your application will be considered on a case by case basis and may require an interview. *This is with the exception of ESFA approved Level 2 Maths and English qualifications, which are mandatory.

Course offers can only be made if approved Level 2 certificates are provided prior to the start of the course.
For further details about which qualifications meet Level 2 requirements download this document from: the gov.uk website

Apply now