The following topics will be covered in the module:



Essential themes, case studies, business examples, and terminology

Practical tools and a contrast of their functions

Application examples and cutting-edge technologies used in the commercial environment

Advanced data gathering techniques (sampling and crawling)

Approaches for data curation and quality assurance

Assessment frameworks

Advanced statistical analysis

Manual and crowdsourcing techniques

Advanced analytics of data (statistical modeling, basic concepts, experiment design, and common pitfalls)

Complex use of interpreting data (visualization techniques, and pitfalls)

Advanced maths and arithmetic

Uses of Machine Learning

Written Report – A written report based on a practical case study. Students will be given a business case study for which they need to do the following tasks. Show clear understanding of the case study through applying key data science approaches to it. The approach must include a coverage of analysis, design and practical implementation through appropriate tools. The written report must discuss as well statistical and machine learning roles in the practical aspects, and demonstrate how the final solution considers commercial aspects. Formative feedback will be provided throughout. There is one summative submission of the report (Learning Outcomes 1 to 4).

Demonstrate knowledge and comprehension of key data science ideas, including techniques, methods, and use cases (and be able to apply tools appropriately in solving related problems)

Knowledge and Understanding,
Problem Solving, Application

Intelligently evaluate methods and themes related to data gathering, sampling, quality evaluation, and data repair

Enquiry,
Reflection

Show knowledge and comprehension of themes related to statistical analysis and machine learning applied to complex issues and problems

Learning,
Application

Demonstrate knowledge and comprehension of large-scale data management and stream processing themes used commercially

Communication,
Learning

Theory will be delivered via lectures and supported by practical classes, seminars and discussion groups. In addition, you will be provided with a range of resources for independent study such as case studies, academic papers and industry stories. There will be a mixture of practical and theoretical formative exercises which will help you build your knowledge and confidence as well as preparing you for the summative assessment.

Open source data analysis tools

Timbers, T. et. al. (2022), Data science: A first introduction. CRC Press

Braschler, M. et. al. (2019), Applied Data Science. Springer International Publishing

Kroese, D. P., et al.(2019), Data science and Machine Learning: Mathematical and Statistical Methods, Chapman and Hall, CRC Press

Clarke, E. (2022), Everything Data Analytics-A Beginner's Guide to Data Literacy: Understanding the Processes That Turn Data Into Insights (All Things Data), ¿Kenneth Michael Fornari

Aspen-Taylor, S. (2022), Data and Analytics Strategy for Business: Unlock Data Assets and Increase Innovation with a Results-Driven Data Strategy, Kogan Page; 1st edition

Theobald, A. (2022), Data Analytics for Absolute Beginners: A Deconstructed Guide to Data Literacy: (Introduction to Data, Data Visualization, Business Intelligence & ... Science, Python & Statistics for Beginners), Independently published¿¿

During the semester, additional readings are announced.

This module addresses a combination of tools, arithmetic, algorithms,¿and machine learning approaches that contribute and make up the area of data science. Through this combination, we can discover hidden patterns or penetrations in unprocessed data that can be applied to important judgments in a business. You need to be knowledgeable in computer science, statistical analysis, mathematics, and information science to become a data scientist, and this module will address all key aspects applying them to business contexts.