DATA SCIENCE AND STATISTICS FOR SPORT
SPE2DSS
2020
Credit points: 15
Subject outline
In this subject, you will explore research methods, statistical analysis and data science techniques specific to sport and exercise science. You will be taught various research designs and statistical approaches that will allow you to interpret data to apply results in a practical environment. The subject will teach you how to perform various data handling techniques, develop statistical models and data visualisation techniques to interpret data in sport and exercise context.
School: Allied Health, Human Services & Sport (Pre 2022)
Credit points: 15
Subject Co-ordinator: Minh Huynh
Available to Study Abroad/Exchange Students: Yes
Subject year level: Year Level 2 - UG
Available as Elective: No
Learning Activities: N/A
Capstone subject: No
Subject particulars
Subject rules
Prerequisites: Students must be admitted in HBSES, HBESB or HZESPB and must have passed HLT1RAE All other enrolments require subject coordinator's approval
Co-requisites: N/A
Incompatible subjects: HLT2IEP
Equivalent subjects: N/A
Quota Management Strategy: N/A
Quota-conditions or rules: N/A
Special conditions: N/A
Minimum credit point requirement: N/A
Assumed knowledge: N/A
Learning resources
Statistics for sports and exercise science: a practical approach
Resource Type: Book
Resource Requirement: Prescribed
Author: Newell, J., Aitchison, T., & Grant, S.
Year: 2014
Edition/Volume: N/A
Publisher: Routledge
ISBN: N/A
Chapter/article title: N/A
Chapter/issue: N/A
URL: N/A
Other description: N/A
Source location: N/A
Jamovi
Resource Type: Web resource
Resource Requirement: Prescribed
Author: Jamovi Project (Version 0.9)
Year: N/A
Edition/Volume: N/A
Publisher: Jamovi
ISBN: N/A
Chapter/article title: N/A
Chapter/issue: N/A
URL: N/A
Other description: N/A
Source location: N/A
Career Ready
Career-focused: No
Work-based learning: No
Self sourced or Uni sourced: N/A
Entire subject or partial subject: N/A
Total hours/days required: N/A
Location of WBL activity (region): N/A
WBL addtional requirements: N/A
Graduate capabilities & intended learning outcomes
Graduate Capabilities
Intended Learning Outcomes
Bendigo, 2020, Semester 1, Blended
Overview
Online enrolment: Yes
Maximum enrolment size: N/A
Subject Instance Co-ordinator: Emma Zadow
Class requirements
LectureWeek: 10 - 22
One 2.00 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via blended.
WorkShopWeek: 10 - 22
One 2.00 hours workshop per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
Two 30 minute online tests (1,000 words equivalent)Two 30 min online short answer and multiple choice tests | N/A | N/A | No | 20 | SILO1, SILO2 |
Two written workshop data analysis activities (1,500 words equivalent) | N/A | N/A | No | 40 | SILO1, SILO2, SILO3, SILO5 |
One written data analysis inquiry project (1,500 words equivalent) | N/A | N/A | No | 40 | SILO1, SILO2, SILO3, SILO4, SILO5 |
Melbourne (Bundoora), 2020, Semester 1, Blended
Overview
Online enrolment: Yes
Maximum enrolment size: N/A
Subject Instance Co-ordinator: Minh Huynh
Class requirements
LectureWeek: 10 - 22
One 2.00 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via blended.
WorkShopWeek: 10 - 22
One 2.00 hours workshop per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
Two 30 minute online tests (1,000 words equivalent)Two 30 min online short answer and multiple choice tests | N/A | N/A | No | 20 | SILO1, SILO2 |
Two written workshop data analysis activities (1,500 words equivalent) | N/A | N/A | No | 40 | SILO1, SILO2, SILO3, SILO5 |
One written data analysis inquiry project (1,500 words equivalent) | N/A | N/A | No | 40 | SILO1, SILO2, SILO3, SILO4, SILO5 |