Academic Handbook Course Descriptors and Programme Specifications
NCHNAP796 Data Analytics Capstone Project Course Descriptor
| Course Title | Data Analytics Capstone Project | Faculty | EDGE Innovation Unit (London) |
| Course code | NCHNAP796 | Course Leader | Professor Scott Wildman (interim) |
| Credit points | 60 | Teaching Period | This course will typically be delivered over a 24-week period. |
| FHEQ level | 7 | Date approved | June 2021 |
| Compulsory/Optional | Compulsory | ||
| Prerequisites | None | ||
Course Summary
The culminating capstone project is conceived and executed by the learner in the workplace. The project will be a business-related project based on the learner’s job role in data analytics. Business and change management, professional competencies, leadership, technology management, and data analytics skills will be assessed. The project will culminate with a written dissertation and viva voce exam as per the Digital Technology Solutions Specialist Assessment Plan.
Indicative project topics may include:
- A critical analysis of data storage solutions within a business, making recommendations for technological change.
- Evaluation of analytical algorithms for model building using complex datasets.
- A review of the ethical, legal, governance and social considerations of data analytics in business: current and emerging trends.
Course Aims
- Give learners the opportunity to carry out an independent project on data analytics aligned to a business problem.
- Train learners to write up their findings and ideas accurately, clearly, coherently and to a high-professional standard.
- Train learners to present their own arguments logically and competently, to engage specialist and non-specialist stakeholders.
Learning Outcomes
On successful completion of the course, learners will be able to:
Knowledge and Understanding
| K1di | Comprehensively understand the principles of business transformation and how organisations integrate different management functions in the context of technological change. |
| K1dii | Comprehensively understand the information governance requirements that exist in the UK, and the relevant organisational and legislative data protection and data security standards that exist, and, the legal, social and ethical concerns involved in data management and analysis. |
| K1diii | Understand own employer’s business objectives and strategy, its position in the market and how own employer adds value to its clients through the services and/or products they provide. |
| K2di | Systematically understand the strategic importance of technology-enabled business processes, and how they are designed and managed to determine a firm’s ability to compete effectively. |
| K2dii | Justify the value of technology investments and apply benefits management and realisation. |
| K2diii | Comprehensively understand technology road-mapping concepts and methods and how to apply them. |
| K2div | Systematically understand how to monitor technology related market trends and research and collect competitive intelligence. |
| K3di | Comprehensively understand how key algorithms and models are applied in developing analytical solutions and how analytical solutions can deliver benefits to organisations. |
| K3dii | Systematically understand the principles of data driven analysis and how to apply these. Including the approach, the selected data, the fitted models and evaluations used to solve data problems. |
| K3diii | Systematically understand the properties of different data storage solutions, and the transmission, processing and analytics of data from an enterprise system perspective. Including the platform choices available for designing and implementing solutions for data storage, processing and analytics in different data scenarios. |
| K3div | Systematically understand how relevant data hierarchies or taxonomies are identified and properly documented. |
| K3dv | Comprehensively understand the concepts, tools and techniques for data visualisation, including how this provides a qualitative understanding of the information on which decisions can be based. |
| K4di | Understand role of learning and talent management in successful business operations. |
| K4dii | Comprehensively understand the role of leadership in contemporary technology-based organisations. |
| K4diii | Understand the personal leadership qualities that are required to establish and maintain an organisation’s technical reputation. |
| K4div | Comprehensively understand the role of leaders as change agents and identify contributors to successful implementation. |
Subject Specific Skills
| S1di | Identify, document, review and design complex IT enabled business processes that define a set of activities that will accomplish specific organisational goals and provides a systematic approach to improving those processes. |
| S1dii | Evaluate the significance of human factors to leadership in the effective implementation and management of technology-enabled business processes. |
| S1diii | Document and describe the data architecture and structures using appropriate data modelling tools, and select appropriate methods to present data and results that support human understanding of complex data sets. |
| S1div | Be competent at negotiating and closing techniques in a range of interactions and engagements, both with senior internal and external stakeholders. |
| S2di | Apply broader technical knowledge combined with an understanding of the business context, and how it is changing, to deliver to the company’s business strategy. |
| S2dii | Deliver workplace transformations through planning and implementing technology-based business change programmes including setting objectives, priorities and responsibilities with others in an area of technology specialism. |
| S2diii | Create and implement innovative technological strategies to support the development of new products, processes and services that align with the company’s business strategy, and develop and communicate compelling business proposals to support these. |
| S2div | Undertake analytical investigations of data to understand the nature, utility and quality of data, and develop data quality rule sets and guidelines for database designers. |
| S2dv | Formulate analysis questions and hypotheses which are answerable given the data available and come to statistically sound conclusions. |
| S2dvi | Conduct high-quality complex investigations, employing a range of analytical software, statistical modelling & machine learning techniques to make data driven decisions solve live commercial problems. |
| S3di | Design and develop technology roadmaps, implementation strategies and transformation plans focused on digital technologies to achieve improved productivity, functionality and end user experience in an area of technology specialism. |
| S3dii | Develop own leadership style and professional values that contributes to building high performing teams. |
| S3diii | Professionally present digital and technology solution specialism plans and solutions in a well-structured business report. |
| S3div | Demonstrate self-direction and originality in solving problems, and act autonomously in planning and implementing digital and technology solutions specialist tasks at a professional level. |
| S3dv | Identify and select the business data that needs to be collected and transitioned from a range of data systems; acquire, manage and process complex data sets, including large-scale and real-time data. |
| S4di | Negotiate and agree digital and technology specialism delivery budgets with those with decision-making responsibility. |
| S4dii | Develop and deliver management level presentations which resonate with senior stakeholders, both business and technical. |
| S4diii | Demonstrate effective technology leadership and change management skills for managing technology driven change and continuous improvement. |
| S4div | Scope and deliver data analysis projects, in response to business priorities, create compelling business opportunities reports on outcomes suitable for a variety of stakeholders including senior clients and management. |
Transferable and Professional Skills
| T1di | Establish high levels of performance in digital and technology solutions activities. |
| T1dii | Be results and outcomes driven to achieve high key performance outcomes for digital and technology solutions objectives. |
| T2di | Inspire and motivate others to deliver excellent technical solutions and outcomes. |
| T2dii | Promote a high level of cooperation between own work group and other groups to establish a technology change led culture. |
| T2diii | Develop and support others in developing an appropriate balance of leadership and technical skills. |
| T2div | Create strong, positive relationships with team members to produce high performing technical teams. |
| T2dv | Consistently display an excellent level of technical proficiency in written English and command of scholarly terminology, so as to be able to deal with complex issues in a sophisticated and systematic way. |
Teaching and Learning
The contact hours on this course are formed predominantly of supervisory meetings, typically 4 x 1 hour.
Learners are expected to carry out independent research into the topic.
Readings should include a mix of books, journal articles, policy papers and other relevant documents, depending on the topic and the approach taken in the dissertation.
Course information and supplementary materials are available on the University’s Virtual Learning Environment (VLE).
Learners are required to attend and participate in all the formal and timetabled sessions for this course. Learners are also expected to manage their directed learning and independent study in support of the course.
The course learning and teaching hours will be structured as follows:
- Off-the-job learning (24 days x 7 hours) = 168 hours (e.g. 1 day per week for 24 weeks)
- On-the-job learning (48 days x 7 hours) = 336 hours (e.g. 2 days per week for 24 weeks)
- Private study (4 hours per week for 24 weeks) = 96 hours
- Total 600 hours
Assessment
Formative
Learners will be formatively assessed during the course by means of set assignments. These will not count towards the final degree but will provide learners with developmental feedback.
Summative
| AE | Assessment Type | Weighting | Online submission | Duration | Length |
| 1 | Dissertation | 50% | Yes | 10 days | 10,000 words +/- 10% |
| 2 | Viva Voce exam | 50% | Yes | 90 mins +/- 10% | – |
Feedback
Learners will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Learners will also attend a formal meeting with their Academic Mentor (and for apprentices, including their Line Manager). These bi or tri-partite reviews will monitor and evaluate the learner’s progress.
Feedback is provided on summative assessment and is made available to the student either via email, the VLE or another appropriate method.
Indicative Reading
Note: Comprehensive and current reading lists for courses are produced annually in the Course Syllabus or other documentation provided to learners; the indicative reading list provided below is used as part of the approval/modification process only.
Books
- Preece, R., 1994. Starting Research : An Introduction to Academic Research and Dissertation Writing. London, New York : Pinter Publishers
- Stephan F. M., and Smith, I., 2019. A Practical Guide to Dissertation and Thesis Writing. Newcastle upon Tyne, England : Cambridge Scholars Publishing
- Dubber, M., Pasquale, F., and Das, S., 2020. The Oxford Handbook of Ethics of AI. New York, New Jersey : Oxford University Press
Journals
Learners are encouraged to read material from relevant journals on postgraduate dissertations and data analytics as directed by their course leader.
Electronic Resources
Learners are encouraged to consult relevant websites on postgraduate dissertations and data analytics.
Indicative Topics
Learners will study the following topics:
- Data Analytics
- Professional context
- Business strategy
| Title: NCHNAP796 Data Analytics Capstone Project Course Descriptor
Approved by: Academic Board Location: Academic Handbook/Programme specifications and Handbooks/ Postgraduate Apprenticeship Programmes/MSc Digital & Technology Solutions Programme Specification/Course Descriptors |
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| Version number | Date approved | Date published | Owner | Proposed next review date | Modification (As per AQF4) & category number |
| 3.0 | October 2022 | January 2023 | Scott Wildman | June 2026 | Category 1: Corrections/clarifications to documents which do not change approved content or learning outcomes.
Category 3: Changes to Learning Outcomes. |
| 2.1 | May 2022 | May 2022 | Scott Wildman | June 2026 | Category 1: Corrections/clarifications to documents which do not change approved content. |
| 2.0 | January 2022 | April 2022 | Scott Wildman | June 2026 | Category 3: Changes to Learning Outcomes |
| 1.0 | June 2021 | September 2021 | Scott Wildman | June 2026 | |