LO1. Demonstrate a systematic understanding of univariate analytics and how this can be used within contextual relevancy.¿
LO2. Demonstrate a systematic understanding of bivariate analytics and how this can be used within contextual relevancy.¿
LO3. Apply methods and techniques learned to review and consolidate a contextual understanding in devising and sustaining ideas for development in a complex decision-making process.
LO4. Critically evaluate data implementation and delivery of quantitative and qualitative research to make and communicate informed judgements.

Assessment 1 – Class Test
Students will be tasked with completing a series of Univariate analysis to answer questions. Evidence of appropriate methods used, and correct answers will both be graded.
LO1

Assessment 2 – Class Test
Students will be tasked with completing a series of Bivariate analysis to answer questions. Evidence of appropriate methods used, and correct answers will both be graded.
LO2

Assessment 3 – Class Test
Students will be tasked with answering several scenario-based questions from a return-on-investment perspective. They will require knowledge of both univariate and bivariate analysis methods to create well supported ROI suggestions. Evidence of appropriate methods used for analysis as well as suitable suggestions for ROI will be graded.
LO3

Assessment 4 – ROI Report
Students will develop a report detailing the return on investment and value that may be gained by taking a specific strategy relevant to a desired area. The ROI report will be backed by substantial data analytics that justify this as a sensible and informed business decision.
LO4

The module aims to develop students understanding of the data behind esports and provides a clear base knowledge of some key performance data involved within the esports ecosystem. The module will provide students with a grounded understanding of how we select data and how it can be used in range of situations.

Within this module we will be looking at:¿¿
- Data and information:¿sources of data; problem-solving and decision-making; decision-modelling; quantitative techniques
- Techniques for data collection: sampling; statistical analysis; trends and forecasts; observation; secondary data collection;¿data analysis and evaluation
- Patterns in Data: changes and relationships in data; forecasting; correlation analysis; regression analysis

Data drives decisions. Learn how Esports and adjacent industries identify and use key performance metrics within data, and the key principles of analysis that contribute to an employable skillset. Develop your understanding in data collection, big data development, statistical analysis, presenting findings and how these all contribute to decision making in your desired discipline.

Students will be expected to engage in a variety of learning strategies, including but not limited to:
- Tutor led formal presentations
- Workshops and group-based tutorials
- Discussion/debate cantered learning
- Participation in group activities, including presentations and discussion panels
- Directed reading
- Self-directed/independent research

- Ankam, V. (2016) Big data analytics: a handy reference guide for data analysts and data scientists to help obtain value from big data analytics using Spark on Hadoop clusters. 1st ed. Birmingham: PACKT Publishing.
- Barlow, J. (2015) Data Analytics in Sports. 1st edition. O’Reilly Media, Inc.
- EMC Education Services (2015) Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, John Wiley & Sons
- Geng, H. (2017) Internet of Things and Data Analytics Handbook. New York: John Wiley & Sons, Incorporated.
- Kennedy, H. and Engbretsen, M. (2020) Data Visualization in Society. Edited by H. Kennedy and M. Engbretsen. Baltimore, Maryland: Project Muse. doi: 10.1515/9789048543137.
- Rines, S. and Miller, H. (2007) Driving business through sport / Part 1, European sports marketing data. London: International Marketing Reports.

University Library
IT
Blackboard
Reading list
VLE learning support material to be provided for independent /self-directed learning.
Module handbooks
Open Textbook Library
Specialised Teaching Labs