Module Title:Introduction to Data Analysis for Sport
Language of Instruction:English
Credits: 5
NFQ Level:8
Module Delivered In 7 programme(s)
Teaching & Learning Strategies: This module will be taught through practical classes in computer labs. Students will be expected to complete problem sheets to enforce learning. Relevant notes, examples and resources will be available on Blackboard.
Module Aim: The aim of this module is to develop students' mathematical and statistical skills with a view to using these skills to analyse sports data. Students will be introduced to the areas of data visualisation, descriptive statistics and inferential statistics. The students will also be introduced to the use of statistical software for data analysis.
Learning Outcomes
On successful completion of this module the learner should be able to:
LO1 Describe basic concepts in statistics, data visualisation and data analysis.
LO2 Evaluate and apply key descriptive analysis techniques when carrying out analysis of sports data.
LO3 Evaluate and apply key inferential statistical techniques when carrying out analysis of sports data.
LO4 Solve well-formed problems by selecting the appropriate techniques and presenting the answer in a sporting context.
Pre-requisite learning
Module Recommendations

This is prior learning (or a practical skill) that is recommended before enrolment in this module.

No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed.
No requirements listed
 

Module Content & Assessment

Indicative Content
Introduction to Statistics
Different data types, tabulation of data, and sampling. Measures of central tendency and dispersion including mean, median and standard deviation.
Data Visualisation
Description of different data visualisation techniques, their purpose and when they are suitable to use. Best practices in data visualisation.
Inferential Statistics
Application of correlation, linear regression and hypothesis testing to analysing sports data.
Computer Practicals
Application of theoretical material using relevant software.
Assessment Breakdown%
Continuous Assessment100.00%
Continuous Assessment
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Other Learners will be required to demonstrate achievement of the learning outcomes through continuous assessment. This work may take the form of a project (individual/group), practical exam, presentation, case analysis, poster presentation but is not limited to these formats. 1,2,3,4 100.00 n/a
No Project
No Practical
No End of Module Formal Examination

ITCarlow reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Frequency Average Weekly Learner Workload
Contact Hours Every Week 3.00
Independent Learning Every Week 6.00
Total Hours 9.00
 

Module Delivered In

Programme Code Programme Semester Delivery
CW_BBSMC_B Bachelor of Arts (Honours) in Sport Management and Coaching 6 Elective
CW_BBSMC_B Bachelor of Arts (Honours) in Sport Management and Coaching 8 Elective
CW_BBSOC_D Bachelor of Arts in Sport Coaching and Business Management (Football) 6 Elective
CW_BBGAA_D Bachelor of Arts in Sport Coaching and Business Management (GAA) 6 Elective
CW_BBRUG_D Bachelor of Arts in Sport Coaching and Business Management (Rugby) 6 Elective
CW_BBSBC_B Bachelor or Arts (Honours) in Sport, Business and Coaching 6 Elective
CW_BBSBC_B Bachelor or Arts (Honours) in Sport, Business and Coaching 8 Elective