SFU-002 - Module 2 (Learning Objectives)

Learning Objectives

During this module we'll outline the key concepts you need to understand before starting the simulation. We'll also cover the goals you should aim to achieve, ensuring you're well-prepared for the tasks ahead.

Remembering

Understanding

Applying

Analysing

Evaluating

Creating

Detailed breakdown

Here's a breakdown of what's to come in the course.

Learning objectives

Remembering: Identify sources and steps for data collection. Understanding: Define the business question or objective.

Preparation Task

(10 minutes)

Remembering: Identify sources and steps for data collection. Understanding: Define the business question or objective.

Preparation Task

(10 minutes)

Remembering: Identify sources and steps for data collection. Understanding: Define the business question or objective.

Preparation Task

(10 minutes)

Applying: Collect data from various sources.Analysing: Organise and compile the collected data.

Research Task

(30 minutes)

Remembering: Identify sources and steps for data collection. Understanding: Define the business question or objective.

Preparation Task

(10 minutes)

Applying: Clean and format the data, and identify key metrics.Analysing: Use visual tools to analyse trends and perform SWOT analysis.

Analysis Task

(30 minutes)

BACK

NEXT

Remembering: Identify sources and steps for data collection. Understanding: Define the business question or objective.

Preparation Task

(10 minutes)

Creating: Develop insights and actionable recommendations based on data analysis.Evaluating: Assess the findings and provide recommendations.

Create Task

(20 minutes)

Remembering: Identify sources and steps for data collection. Understanding: Define the business question or objective.

Preparation Task

(10 minutes)

Creating: Compile a detailed report and prepare a presentation.Evaluating: Share and get feedback to improve the final output.

Document & Present

(15 minutes)

Remembering: Identify sources and steps for data collection. Understanding: Define the business question or objective.

Preparation Task

(10 minutes)

Evaluating: Reflect on the learning experience and its application.Understanding: Consider how the exercise enhanced skills and identify areas for further improvement.

Reflection Task

(10 minutes)

BACK

Detailed breakdown

Here's a breakdown of what's to come in the course.

Learning objectives

NEXT

Conclusion

By the end of this activity, you will gain a strong understanding of sports analytics, from collecting and analysing data, visualising key findings, to making predictive analysis. You would have developed practical skills and deepened your understanding of sports analytics, which will significantly contribute to your potential career in this domain.Now that you're all prepped, mark this section as complete and let's make a start on the simulation module!

Learning objectives

• Identify key online data sources for sports analytics (e.g., stats.com, ESPN, etc.).
• List the steps involved in conducting sports analytics, such as identification of a problem, collection and cleaning of data, analysis, visualisation, predictive analysis, and presentation of findings.

• Summarise key findings from the data and create visualisations to illustrate the findings.
• Develop a detailed report and a presentation summarising the analysis, visualisations, and predictive analysis.
• Reflect on your learning, thinking about how the insights and skills gained can be applied to real-world sports analytics scenarios and how you can improve your analytical and predictive modelling skills for the future.

• Choose a specific sports team and a key question or problem related to the team's performance.
• Collect sports data from various online sources and compile it in a spreadsheet program.
• Use spreadsheet functions to clean and organise the data for analysis.

• Explain why sports analytics is essential to understanding a team's performance and making predictions.
• Describe how different factors like players' statistics, game outcomes, and opposing team's performance impact a team's success or failure.

• Assess the quality and reliability of the collected data.
• Evaluate the team’s performance based on the analysed data and identified trends.
• Make informed recommendations for the team based on the results of the analysis and predictive modelling.

• Organise and format the collected data for further analysis.
• Use data visualisation tools to analyse the data and identify patterns, trends, and correlations.
• Use the identified trends and correlations to make predictive analysis about the team's future performance.