In this lesson, you'll learn how to choose an interdisciplinary topic that sparks your interest, develop a testable hypothesis to guide your investigation, consider important data ethics to ensure responsible practices, and begin planning how to collect your data.

This lesson sets the stage for your analytics artefact, where you'll analyse data to draw meaningful insights and inform decision-making.

As you're working in teams, remember to collaborate effectively, sharing ideas and assigning roles to make the most of everyone's strengths. By the end of this lesson, you'll have a solid plan for your project, ready to dive into data processing in upcoming sessions.

Work with your team to assign roles for this planning phase. This builds on the team formation from the overview lesson.

Roles could include:

• Researcher (gathers data sources and identifies potential datasets)
• Analyst (plans data analysis methods, like calculating mean, median, mode)
• Ethicist (leads discussions on data ethics)
• Documenter (compiles the plan)

Discuss strengths: If someone excelled in the Analytics unit, assign them to planning data processing or visualisation.

Start by selecting a topic that interests your team and relates to real-world issues. Good topics are interdisciplinary, meaning they connect computing with other subjects like environmental science, health, or social trends. This will make your project more engaging and allow you to apply analytics skills effectively.

For example, you could explore 'The impact of social media usage on study habits among teenagers' or 'Patterns in local weather data affecting crop growth in your area'. Other ideas might include 'The relationship between exercise frequency and sleep quality' or 'Trends in recycling habits in your school community'.

Guided Steps:

1. Brainstorm 3-5 ideas as a team, drawing from current events, personal interests, or school subjects.
2. Ensure the topic allows for data collection and analysis (e.g., calculating frequency, mean, median, mode as per outcome 3.4) – think about what numbers or categories you'll need.
3. Check feasibility: Can you gather data ethically and within time limits? Consider if you'll need surveys, online datasets, or observations, and if it's practical for your team.

A hypothesis is a clear, testable statement about what you expect to find from your data. It acts as a guiding prediction for your project, helping you focus your data collection and analysis. It should be based on your chosen topic and directly relate to the kinds of calculations you'll perform, such as finding frequencies, means, medians, or modes.

Guide to Formation:

• Make it specific: Clearly state the variables involved, e.g., 'Students who use social media more than 2 hours a day have lower average study times.'
• Ensure it's measurable: Tie it to data you can quantify, like comparing means of study hours or frequencies of certain behaviours.
• Make it falsifiable: Design it so that data could potentially disprove it, ensuring it's a true test rather than an assumption.
• Base it on background knowledge: Draw from what you know or preliminary research about the topic to make an educated guess.

Ethics are crucial in data handling. Always respect privacy, obtain consent, and avoid harm.

Key Principles:

• Consent: If collecting from people, explain how data will be used and get permission.
• Privacy: Anonymise data; don't collect unnecessary personal info.
• Bias: Ensure your data sources are diverse to avoid skewed results.
• Legal: Follow data protection laws like GDPR.