Data Science (Apprenticeship)

This module is designed to allow flexibility of study, to enable employed students to gain credit for work based activities, and to contribute to the continued development of academic and professional skills. The module aims to develop student understanding of the relationships and dependencies that exist between different functions within an organisation, and their contribution to the overall performance of the business, with particular reference to the key functions within the organisation in which they are employed. 

Students will work individually, in groups and with their workplace mentor to develop the knowledge and skills required to analyse, and present, ideas and coherent arguments both orally and in writing. Students will build on their communication, negotiation and reflective practice skills, to support their on-going personal, academic and professional development.

This module is designed to allow flexibility of study, to enable employed students to gain credit for work-based activities, and to contribute to the continued development of academic and professional skills. The aim of this work-based learning module is to help students develop a deeper understanding of how their organisation operates including examining the relationships and dependencies that exist between different functions within an organisation, and to help them to recognise how their own contribution can form the foundations of a successful career within the company. To achieve this, students will reflect on themselves, their skills, and how they fit within their company. With support from a work-based mentor, students will identify their development needs, manage their own development, and reflect critically on their learning. This module provides the foundation for continuing personal and professional development building confidence to provide students with the potential to maximise both academic and career aspirations.

This module introduces students to quantitative ideas and procedures which are fundamental to the work of the professional data scientist. It will take a real-world approach to understanding the value and meaning of data and collecting and preparing data for processing. Students will be introduced to basic statistical concepts and analytical software tools for specific data analysis tasks. This will provide students with a practical knowledge of applied computing which will help to underpin more advanced study found later in the programme modules. It will also support longer term academic and professional development. The module enables students to explore analytical methods and appreciate critically the professional literature and research in the field of data analysis.

The emergence of the digital economy has unlocked new opportunities for businesses, whilst creating new modes of competition in both traditional and new sectors of the economy.  The aim of this module is to impart an understanding of digital business together with the practices and processes required to develop effective digital strategies.  Identifying multiple technologies for integration into business, juxtaposed with the development of new digital strategies is central to corporate success, however, this is often a complex task.  This module provides insight into the emergence of digital business, key concepts, technologies, and strategic organisation to develop a multidisciplinary appreciation of how new technologies can directly shape businesses and processes.

This module will introduce technical students to programming and testing concepts. Students will develop an understanding of the general principles of how programs and projects are delivered within the IT services industry and the application of the concepts of testing and programming. The module will develop introductory skills using a specific but not defined programming language and platform. Undertaking the module will develop the student’s skills in developing and testing programmes using industry techniques.

This module imparts an understanding of operating systems including concepts such as scheduling, concurrency and synchronisation, memory management, input and output systems, kernel security and file systems.  Fundamentals of data structures and core algorithms and analysis are also explored.

This module imparts an understanding of analysis and design concepts that are essential for developing and implementing software and database systems.  Design concepts and procedures such as Business Rules, Requirements Analysis, Data Modelling, Relational Data Modelling, Object Orientated Analysis and SQL will be explored.  Students will also learn how to apply Unified Modelling Language (UML) within different computing scenarios.

This module introduces probability and distribution theory and then shows how these allow statistical inference from data. The first part of the module defines probability via axioms and introduces random variables and distributions of practical importance, including multivariate distributions. The second part then shows how data can be modelled as random variables, leading to basic methods of frequentist or, in principle, Bayesian inference. The software R is used to explore properties of distributions and to make basic statistical inferences from data.

This module introduces the field of artificial intelligence and the fundamental concepts and techniques in the areas of problem solving, knowledge representation and machine learning.  Agents, Search, Planning, Knowledge Representation and Bayesian Artificial Intelligence are explored.

The module develops theoretical and practical skills in software engineering principles including abstraction, encapsulation, aggregation and inheritance.  Students will learn the processes from analysis to design, implementation, testing and documentation together with software quality principles. 

This module introduces the activities of preparing data for presentation through cleansing and validation processes.  It introduces students to presentational techniques for visualisation as a form of recording, understanding and communication of transformed data. Visualisation techniques are important because they can present large and overwhelming amounts of multi-source and multi-format data. The business need is for data to be presented in perceptible, comprehensible, relevant and usable visual forms to communicate complex ideas that support decision making. The module will cover the presentation of data using industry standard techniques, and the advantages and limitations of a wide range of visualisation approaches such as basic statistical charts through to more complex formats. The choice and selection of a range of visual formats is considered and practised using examples related to the business environment. Students will have the opportunity to develop skills with visualisation tools used in the workplace.

In this module you will develop professional skills in applying data analysis techniques and processes involved in conducting projects within the business environment.  It will introduce some of the most widely used predictive modelling techniques and their core principles.  You will form a solid foundation of predictive data analytics using relevant tools and technology for building statistical or machine learning models to make predictions based on data.  You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modelling, an essential skill valued in business.  This module will also help understand and unlock the power of large datasets, acquiring some practical skills in data science to create data visualisations and carry out analyses.

This module is designed to allow flexibility of study, to enable employed students to gain credit for work based activities, and to contribute to the continued development of academic and professional skills.  The aim of this work-based learning module is to build on the student’s existing knowledge of Project and Consultancy Management and Project Management documentation. Through undertaking research into their own organisation students will develop both a theoretical and practical understanding of project and consultancy management including; the importance of effective liaison with delivery project managers and management of client expectations. In completing this module students will draw upon learning from other modules, and reflect on how this module can support the students’ professional development and enhance their performance as effective practitioners. The module will encourage students to demonstrate the use of different management and communication styles within a team and project environment.  Students will build upon previous personal and interpersonal skills and assess the impact of their own communication styles and team member’s styles on team dynamics. Students will reflect on the challenges of managing a project for themselves with the view to developing students’ awareness of application in the contemporary business world.

This module will explore both technical and client management.  Students will explore how to plan, design and manage computer networks with an overall focus on the services and capabilities that network infrastructure solutions enable in an organisational context including; identification of network security risks and their resolution.  Students will also understand concepts of technology and client management and recognise any inter-relationships between them. The module is also aimed at helping students to develop some of the skills involved in managing and leading people, thereby further cultivating the self-awareness that characterises outstanding managers and leaders.

The first half of this course builds on the material covered in probability and statistics by investigating several aspects of statistical distribution theory. The aim of the second half of this module is to describe the theory and methods of using linear statistical models in analysing data to understand the influence of one or more explanatory variables and to make predictions about the response.

Cloud computing describes a new supplement, consumption, and delivery model for IT services based on the Internet. Cloud computing is a consequence of the ease-of-access to remote computing sites provided by the Internet. Within this context, the aim of this module is to develop student understanding of cloud technologies, infrastructure and deployment. The values that cloud computing may bring to an organisation will be evaluated. An array of cloud products and services will be appraised in the context of how they may apply to different types of organisation and their operational functions. Students will appraise an array of methods used to provide virtual storage and network virtualisation. The security and ethical challenges inherent in an organisational transformation to cloud computing will be evaluated.

This module covers the two major data driven business prediction topics of forecasting and simulation.  Forecasting is the analysis of trends in data and is a subject related to probability, risk and uncertainty. The module covers the main numerical forecasting methods and their accuracy limitations.  Less formal judgemental methods are also covered. Simulation, in a business context, is about constructing data driven models to emulate real world systems with sufficient fidelity and validity, so that the possible impacts caused by changes to its component variables can be explored experimentally. The module will cover the principles of simulation and simulation model building. Students will have the opportunity to develop advanced spreadsheet modelling and problem structuring methods.

This module provides students with a deeper understanding of how data is used by strategic decision makers. Students will study the concepts of ‘Big Data’ and data storage. The current strategic issues of concern to the data scientist will be considered. Students will also examine the analysis and storage issues for unstructured data. The module will conclude with a data analytics case study where the student will be required to work through the life cycle of the data analytics case study using appropriate techniques and methods, reporting on the findings and making critical recommendations to a given strategic stakeholder.

This module is primarily concerned with the fundamentals of Mathematical Optimisation and practical aspects of numerical solutions arising from real-world problems. The course will focus on:

• Convex sets, functions, optimisation problems.
• Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems.
• Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods.
• Applications to statistics and finance.

This Data Science Project demonstrates the application of relevant professional skills and behaviours to meet the outcomes in accordance with the Data Scientist (integrated degree) standard.  Evidence of appropriate project planning and research methodologies will be shown together with an evaluation of the processes followed and recommendations for future activities.

The Data Scientist Portfolio will provide substantive evidence from real-work projects and enable the apprentice to demonstrate how they have applied data science in a real-work environment. It is also used to help the apprentice to answer questions in the Professional Discussion. It will showcase elements of work that describe the apprentice’s competency against knowledge, skills and behaviours identified by the Institute for Apprenticeships and Technical Education under their Data Scientist (integrated degree) standard.

The real-work projects are typically undertaken alongside the apprentice’s normal duties with their employer. This assessment has two elements: Knowledge Test and Professional Discussion. The latter is informed by a portfolio, which is delivered in the form of an ePortfolio with a presentational element.

Issues with regard to confidentiality and/or security will be addressed between the University, employer and apprentice allowing the data scientist portfolio to have business value from being undertaken using real-work data.