This project will identify and record data science related undergraduate and postgraduate courses across College of Science and Engineering Schools.

Courses will be classified under the following headings;

Data Awareness (reflective)

Individuals have a basic level of knowledge and understanding of the principles underlying data processing, how data can be acquired, its structure and quality and how these affect their own work, but will normally not analyse data themselves or build the solutions that apply data science methods to a concrete problem.

Individuals with data awareness will look to those with data skills for guidance or data science for expertise, but need to be able to consider the role of insight and knowledge derived from data by these experts.

Data Skills (active)

Individuals who possess data skills are data-literate and actively interact with data e.g. to capture or apply statistical measures and use tools to work with data but do not possess the skills that allow them to pro-actively extract knowledge and insights from data in new ways. They are, however, capable to work with this knowledge and insights, and also to critically reflect on the data science methodology applied.

Data skills can include skills in using tools that offer such things as data analysis, data management and more, but only where that’s possible using pre-packaged, user-friendly tools; people without deep mathematical or computational skills can use.

Data Science (pro-active)

Data Science is not a single process or method but rather a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. These typically involve elements of statistical modelling, machine learning, pattern recognition, data management, cybersecurity, AI, and data-intensive computing.

Data Scientists combine a number of data skills to develop a workflow to extract knowledge and insights from data (often at scale) and will be proficient in the mathematical foundations to build models of the phenomena that generate the data and the computational techniques and tools needed to build effective implementations of data analytics, prediction, decision support or automated decision making tools.

 

Further work to identify continued professional development (CPD) and executive education courses across the College (mostly delivered ad hoc and not currently recorded on systems) will develop over the coming months

 

Current project status

Report Date RAG Budget Effort Completed Effort to complete
December 2020 BLUE 0.0 days 0.0 days 0.0

Project Info

Project
Systematic review of data science courses, CPD and executive education
Code
BAY101
Programme
Bayes Centre Data Driven Education (BED)
Project Manager
Kate Robertson
Project Sponsor
Michael Rovatsos
Current Stage
Close
Status
Closed
Project Classification
Transform
Start Date
01-Jan-2020
Planning Date
n/a
Delivery Date
31-Oct-2020
Close Date
31-Dec-2020
Programme Priority
1
Overall Priority
Normal
Category
Compliance

Documentation

Not available.

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