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Cover Page
B1705
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Cover Page
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Introduction
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Schedule
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Assessment
Week One
5
Outline
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Data Analysis and Interpretation
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Regression Analysis
8
Introduction to Multivariate Analysis
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Data Analysis and Interpretation - Practical
10
Regression Analysis - Practical
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Introduction to Multivariate Analysis - Practical
12
Research Methods in the Social Sciences
13
Forms of Research in Sport Data Analytics
14
A Typology of Research Questions
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Identifying a Research Topic
Week Two
16
Outline
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Factor Analysis
18
Cluster Analysis
19
Factor Analysis - Practical
20
Cluster Analysis - Practical
21
Conducting a Literature Review
22
Identifying Gaps in Current Knowledge
23
Developing a Conceptual Framework
24
Citing and Referencing Relevant Sources
Week Three
25
Outline
26
Discriminant Analysis
27
Canonical Correlation
28
Discriminant Analysis - Practical
29
Canonical Correlation - Practical
30
An Introduction to Quantitative Research
31
Variables in Quantitative Research
32
Quantitative Study Designs
33
Sampling Techniques and Data Collection Methods
Week Four
34
Outline
35
MANOVA and MANCOVA
36
Path Analysis and SEM
37
MANOVA and MANCOVA - Practical
38
Path Analysis and SEM - Practical
Week Five
39
Outline
40
Effect Sizes
41
Standardisation and Normalisation
42
Reporting and Disseminating Findings
43
Case Studies, Ethnography, and Phenomenology
44
Grounded Theory and Content Analysis
45
Triangulation and Mixed-Methods Research
Week Six
46
Outline
47
Introduction to Time-Series Analysis
48
Exploratory Time Series Analysis
49
Stationarity and Differencing
50
Introduction to Time Series Analysis - Practical
51
Exploratory Time Series Analysis - Practical
52
Stationarity and Differencing - Practical
53
Validity and Internal Validity
54
External and Construct Validity
55
Criterion Validity
56
Addressing Validity in Research
Week Seven
57
Outline
58
Time Series Forecasting
59
Model Selection and Checking
60
Advanced Time-Series Models and Approaches
61
Decomposition and Moving Averages: Practical
62
Model Selection and Checking - Practical
63
Advanced Time-Series Models - Panel Data Practical
64
Introduction to Ethics in Sport Data Analytics
65
Data and Ethics
66
Ethics and Wearable Technologies in Sport
Week Eight
67
Outline
68
Machine Learning: Introduction
69
Machine Learning: Workflow
70
Machine Learning: Algorithms
71
Machine Learning: Introduction (Practical)
72
Machine Learning: Workflow (Practical)
73
Machine Learning: Algorithms (Practical)
74
Structuring a Research Report
75
Writing the Sections of a Research Report
Week Nine
76
Outline
77
Supervised Learning: Introduction
78
Linear Models for Regression in R
79
Linear Models for Classification
80
Standardisation and Normalisation in ML
81
Linear Models for Regression - Practical
82
Linear Models for Classification - Practical
83
Effective Presentation of Research
84
Peer Review and Publication Process
Week Ten
85
Outline
86
Introduction to Unsupervised Learning
87
Clustering Techniques in ML
88
Dimensionality Reduction in ML
89
Ensemble Methods in ML
90
Week Ten Practical
Additional Material
91
Anova
92
Autocorrelation
93
Area Under the Curve
94
Degrees of Freedom
95
Odds Ratios
B1705
Author
Dr Allan Hewitt
Published
Invalid Date
2
Introduction
