Work-Based Project (Assessment)
| Code | School | Level | Credits | Semesters |
| ZDAT3001 | Computer Science | 3 | 40 | Full Year UK |
- Code
- ZDAT3001
- School
- Computer Science
- Level
- 3
- Credits
- 40
- Semesters
- Full Year UK
Summary
This is the capstone project of the Data Science degree. It is a requirement that this is a work-based project which must be completed prior to the Gateway Review. The project will not include a dissertation, as a separate project report must be delivered by the learner as part of the End Point Assessment. Instead, the assessment will be based on a portfolio of project related artifacts. Learners should consult the relevant project brief each academic year, but an indicative set of deliverables is:
Outline Project Agreement – An agreement between the learner, the university and employer which i) identifies clear organisational needs, ii) scopes the project and ensures that learner has access to necessary system, data, tools and computer services to complete the project and allows achievement of the necessary KSBs.
Ethical and Legal Assessment – Following initial agreement for the project, undertake a review of any legal and ethical considerations, including consideration of local company regulations, and university ethics guidelines.
Initial Project Plan – A suitable plan such as a Gantt Chart, or set of User Stories for delivery of the project.
Software Deliverable – Evidence of a software that has been designed and delivered, with evidence of progress shown in a suitable software repository.
Reflective Log – A weekly, electronic log documenting the progress of the project including design decisions made, problems encountered, reviews against plan and agreed changes to the scope.
Presentation – A demonstration of the data science solution, and delivery of findings from the data analysed that meet organisational needs.
KSB Review – An evaluation to clarify which KSBs have been met through the project completion.
There is an explicit requirement for learners to undertake a project within the workplace as part of their role (on-the-job and off-the-job)
Target Students
Only available to those studying towards the Data Scientist Degree apprenticeship programme
Co-requisites
Modules you must take in the same academic year, or have taken in a previous year, to enrol in this module:
- Becoming a Professional Data Scientist (DATA3001)
- Scaling up Data Science (DATA3002)
- Project Support (DATA3003)
Assessment
- 100% Dissertation: Deliver a significant work-based data science project
Assessed by end of designated period
Learning Outcomes
Be able to plan and manage a significant work-based, data science project.
Can identify suitable questions and hypotheses which will be addressed by a data science solution, and can link these to improving organisation outcomes.
Able to design and implement a data science software system that is efficient (in terms of cost and time), using appropriate techniques, tools and computational resources for processing the type and nature of data.
Can apply knowledge of legal and ethical issues in relation to a data science problem including understanding the limitations of any techniques used.
Can communicate effectively through written work and through effective presentation of results.
Demonstrate a comprehensive understanding of current and developing data science approaches and apply them where appropriate.
KSBs
Knowledge
A Data Scientist must understand:
K1. The context of Data Science and the Data Science community in relation to computer science, statistics and software engineering. How differing schools of thought in these disciplines have driven new approaches to data systems.
K2. How Data Science operates within the context of data governance, data security, and communications. How Data Science can be applied to improve an organisation’s processes, operations and outputs. How data and analysis may exhibit biases and prejudice. How ethics and compliance affect Data Science work, and the impact of international regulations (including the General Data Protection Regulation.)
K3. How data can be used systematically, through an awareness of key platforms for data and analysis in an organisation, including:
- Data processing and storage, including on-premise and cloud technologies.
- Database systems including relational, data warehousing & online analytical processing, “NoSQL” and real-time approaches; the pros and cons of each approach.
- Data-driven decision making and the good use of evidence and analytics in making choices and decisions.
K4. How to design, implement and optimise analytical algorithms – as prototypes and at production scale– using:
- Statistical and mathematical models and methods.
- Advanced and predictive analytics, machine learning and artificial intelligence techniques, simulations, optimisation, and automation.
- Applications such as computer vision and Natural Language Processing.
- An awareness of the computing and organisational resource constraints and trade-offs involved in selecting models, algorithms and tools.
- Development standards, including programming practice, testing, source control.
K5. The data landscape: how to critically analyse, interpret and evaluate complex information from diverse datasets:
- Sources of data including but not exclusive to les, operational systems, databases, web services, open data, government data, news and social media.
- Data formats, structures and data delivery methods including “unstructured” data.
- Common patterns in real-world data.
Skills
A Data Scientist is able to:
S1. Identify and clarify problems an organisation faces, and reformulate them into Data Science problems. Devise solutions and make decisions in context by seeking feedback from stakeholders. Apply scientific methods through experiment design, measurement, hypothesis testing and delivery of results. Collaborate with colleagues to gather requirements.
S2. Perform data engineering: create and handle datasets for analysis. Use tools and techniques to source, access, explore, prole, pipeline, combine, transform and store data, and apply governance (quality control, security, privacy) to data.
S3. Identify and use an appropriate range of programming languages and tools for data manipulation, analysis, visualisation, and system integration. Select appropriate data structures and algorithms for the problem. Develop reproducible analysis and robust code, working in accordance with software development standards, including security, accessibility, code quality and version control.
S4. Use analysis and models to inform and improve organisational outcomes, building models and validating results with statistical testing: perform statistical analysis, correlation vs causation, feature selection and engineering, machine learning, optimisation, and simulations, using the appropriate techniques for the problem.
S5. Implement data solutions, using relevant software engineering architectures and design patterns. Evaluate Cloud vs. on-premise deployment. Determine the implicit and explicit value of data. Assess value for money and Return on Investment. Scale a system up/out. Evaluate emerging trends and new approaches. Compare the pros and cons of software applications and techniques.
S6. Find, present, communicate and disseminate outputs effectively and with high impact through creative storytelling, tailoring the message for the audience. Use the best medium for each audience, such as technical writing, reporting and dashboards. Visualise data to tell compelling and actionable narratives. Make recommendations to decision makers to contribute towards the achievement of organisation goals.
S7. Develop and maintain collaborative relationships at strategic and operational levels, using methods of organisational empathy (human, organisation and technical) and build relationships through active listening and trust development.
S8. Use project delivery techniques and tools appropriate to their Data Science project and organisation. Plan, organise and manage resources to successfully run a small Data Science project, achieve organisational goals and enable effective change.
Behaviours
A Data Scientist demonstrates:
B1. An inquisitive approach: the curiosity to explore new questions, opportunities, data, and techniques; tenacity to improve methods and maximise insights; and relentless creativity in their approach to solutions.
B2. Empathy and positive engagement to enable working and collaborating in multi-disciplinary teams, championing and highlighting ethics and diversity in data work.
B3. Adaptability and dynamism when responding to varied tasks and organisational timescales, and pragmatism in the face of real-world scenarios.
B4. Consideration of problems in the context of organisation goals.
B5. An impartial, scientific, hypothesis-driven approach to work, rigorous data analysis methods, and integrity in presenting data and conclusions in a truthful and appropriate manner.
B6. A commitment to keeping up to date with current thinking and maintaining personal development. Including collaborating with the data science community.