ULMS Electronic Module Catalogue |
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module. |
Title | Sports Business Analytics | ||
Code | MKBL709 | ||
Coordinator |
Mr DC Cockayne Marketing (ULMS) David.Cockayne@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2020-21 | Level 7 FHEQ | Whole Session | 15 |
Pre-requisites before taking this module (other modules and/or general educational/academic requirements): |
Modules for which this module is a pre-requisite: |
Programme(s) (including Year of Study) to which this module is available on a required basis: |
Programme(s) (including Year of Study) to which this module is available on an optional basis: |
Teaching Schedule |
Lectures | Seminars | Tutorials | Lab Practicals | Fieldwork Placement | Other | TOTAL | |
Study Hours |
2 |
24 |
26 | ||||
Timetable (if known) |
120 mins X 1 totaling 2
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180 mins X 1 totaling 24
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Private Study | 124 | ||||||
TOTAL HOURS | 150 |
Assessment |
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EXAM | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Essay Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: Yes | -1500 words | 40 | ||||
Executive dashboard Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: Yes | -2000 words | 60 |
Aims |
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The module aims to: Nurture an understanding of the importance of data-driven decision making and its subsequent impact on strategy formulation; Enable students to understand how statistical analysis and data visualisation assist in identifying sport business trends and solutions. |
Learning Outcomes |
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(LO1) Demonstrate knowledge and a broad understanding of the emerging data driven sport business landscape. |
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(LO2) Evidence an understanding of emerging technologies and the potential for disruption and development in the use of data analysis in sports business. |
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(LO3) Apply quantitative analysis to understand and present complex data to support evidenced-based decision making. |
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(LO4) Discuss critically how data visualisation contributes to evidence-based decision making and organisational communications. |
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(LO5) Reflect creatively on how analytical techniques can address sport business problems. |
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(LO6) Ideate and prototype applications of data visualisation techniques in the construction of organisational and executive dashboards and reports. |
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(LO7) Appraise the value of analytics across a range of other business contexts. |
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(LO8) Assess the limitations of data analytics in the decision-making process. |
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(S1) Flexible and adaptable. |
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(S2) A problem solver. |
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(S3) Numerate. |
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(S4) An excellent verbal and written communicator. |
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(S5) IT literate. |
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(S6) Ethically aware. |
Teaching and Learning Strategies |
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The module will primarily be delivered through eight e-lectures/seminars. These will consist of podcasts covering key concepts, theories, and practical applications of quantitative data analysis and visualisation communication techniques. Contemporary software packages will be used to support practical activities linked to lecture content. Individual online tasks and discussion boards will be used to develop and apply learning within the sport industry and the students’ own work contexts. These will be moderated by the module tutor to ensure feedback, and to support the development of the virtual cohort. Readings and contemporary debates surrounding this area will be used to punctuate vocational development, ensuring balance between ‘know-how’ and ‘know-why’. Unscheduled Directed Student Hours: 24 hours Description: The e-Lectures/seminars will equate to 3 hours/week over 8 weeks, undertaken asynchronously. Attendance Recorded: Ye s – tracked via the learning platform. Additionally, one scheduled synchronous seminar will be delivered (if there are issues with time zones another seminar will be provided). Description: The scheduled seminar will equate to 2 hours undertaken synchronously. The date and time of the seminar will be confirmed at the start of the module. Attendance Recorded: Yes – tracked via the learning platform. Self-Directed Learning Hours: 124 hours Description: This will involve directed and independent reading, independent research and assessment preparation. |
Syllabus |
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Data, Big Data, and Disruptive Technologies; |
Recommended Texts |
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Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module. |