Applied Machine Learning Assessment

Code

ZDAT2003

School

Computer Science

Level

2

Credits

20

Semesters

Full Year UK

Summary

This assessment block provides learners with the opportunity to apply AI and Machine learning to data science problems.   There will be three coursework elements of increasing complexity.

Basic, prescriptive coursework
More advanced coursework
Open coursework which is intended to be tailored the apprentice’s role / workplace.

Learners will be encouraged to look for opportunities/data/business questions within their organisation to apply the techniques learned

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:

• AI and Machine Learning (DATA2006)

Assessment

• 100% Coursework 1: AI/ML progressive projects related to the workplace.

Assessed by end of designated period

Learning Outcomes

Demonstrate understanding of a range of AI methods and have a good understanding of how those techniques and the kinds of problems to which they can be successfully applied.

Evaluate which AI methods can be successfully applied to specific problems, and to take problems and associated datasets and solve those problems using AI methods.

Be able to implement AI techniques using languages and systems used in the contemporary workplace.

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.

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.
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

• Adam Walker