Big Data and Cloud Project (Assessment)

Code

ZDAT3002

School

Computer Science

Level

3

Credits

20

Semesters

Full Year UK

Summary

This assessment will allow learners to learn how to build distributed big-data solutions.

It will set progressively more difficult assessments allowing learners to consolidate their learning, build their skills and then demonstrate their creativity in implementing solutions.

Learners are expected to use relevant projects from the workplace which require the use of high performance computing and techniques to address Big Data problems

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:

• Scaling up Data Science (DATA3002)

Assessment

• 100% Coursework: Big Data/Cloud Software Development Coursework

Assessed by end of designated period

Learning Outcomes

Understand the principles that allow the processing of big data sets.

Understand the limitations of big data technologies for distributed processing.

Demonstrate practical skills required to implement big-data solutions using modern large-scale data and compute infrastructures.

KSBs

K3. How data can be used systematically, through an awareness of key platforms for data and analysis in an organisation, including:  

1.     Data processing and storage, including on-premise and cloud technologies.
2.     Database systems including relational, data warehousing & online analytical processing, “NoSQL” and real-time approaches; the pros and cons of each approach.  
3.     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:

1.     Statistical and mathematical models and methods.  
2.     Advanced and predictive analytics, machine learning and artificial intelligence techniques, simulations, optimisation, and automation.  
3.     Applications such as computer vision and Natural Language Processing.
4.     An awareness of the computing and organisational resource constraints and trade-offs involved in selecting models, algorithms and tools.
5.     Development standards, including programming practice, testing, source control.

K5. The data landscape: how to critically analyse, interpret and evaluate complex information from diverse datasets:

1.     Sources of data including but not exclusive to les, operational systems, databases, web services, open data, government data, news and social media.
2.     Data formats, structures and data delivery methods including “unstructured” data.
3.     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.

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.

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.

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