Project Support

Code

DATA3003

School

Computer Science

Level

3

Credits

N/A

Semesters

Full Year UK

Summary

This teaching block provides apprentices with an opportunity to develop independent planning and research abilities synthesising and further developing their project planning and writing skills previously gained in the programme.  

It will allow apprentices to critically analyse and understand the nature of project planning using appropriate tools and methods. It will also allow apprentices to critically analyse research/business problems and propose appropriate research strategies/methods for successfully completing a data science project. Content will be supported through online workshops and individual supervision tutorials. Group online sessions will provide opportunities for formative assessment of progress and sharing of ideas within a team environment.

Target Students

Only available to Data Science apprentices

Assessment

• 100% Assignment

Assessed by end of designated period

Educational Aims

Learning Outcomes

Teaching Goal 1

Be able to plan and manage a significant work-based, data science project.

Teaching Goal 2

Can demonstrate independent research skills, identify suitable questions and hypotheses which will be addressed by a data science solution, and can link these to improving organisation outcomes.

Teaching Goal 3

Demonstrate communicate skills through a formal technical report/poster/oral presentation.

Teaching Goal 4

Can communicate effectively through written work and through effective presentation of results.

KSBs

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.

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.

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.

Conveners

• Dr Simon Kent
• Dr Ria Symonds