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1. How might we decide the best approach when detecting missing data?

2. Are there situations where one approach may be better than another?

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If you were rebuilding the data pipeline, which validation rules or metadata would you add to prevent mixed formats and extreme outliers in the future?

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Describing Data Fundamentals - Tendency and Dispersion

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Describing Data Fundamentals - Tendency and Dispersion

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You have by now used quite a few different libraries as part of your coding exercises, for example:

panda

numpy

matplotlib

seaborn

How are you finding working with these libraries? Are there any issues or pitfalls  you’ve found that you’d like to share that you think your peer group may help with, or that you think may be helpful to others?

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1. There was a lot of materials to go through there. How are you feeling?

2. Do you think your coding skill is up to the necessary standard? Was everything understandable?

3. How might you go about improving this as you progress through this course ?

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1. How could inconsistent encoding of categorical fields affect machine learning model performance?

2. If you were designing this data pipeline from scratch, how would you prevent these issues in the future?

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Now compare the report you’ve written against the model guidance document in your resources section.

1. How did your reports compare to the model answers – were they close?

2. Was there anything you did the model answers didn’t cover, or vice versa?

3. Are there any key takeaways from this activity that you can learn from to develop your business analytics skills further?

4. Did the activity highlight to you the limitations of descriptive statistics? Why? Why not?

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For each of the scenarios below, choose the most appropriate tool from the comparison list (e.g., Excel, Tableau, Power BI, Python, R, SPSS, etc).

Then, briefly justify your choice using the criteria provided.

You may need to do further research of your own to better understand each tool/library combination.

Scenario 1: Executive Presentation

You need to present monthly KPIs to a board of directors with limited technical expertise. The focus is on trends, comparisons, and immediate clarity.

Consider: Clarity, Interactivity, Ease of use, Visual appeal

Scenario 2: Academic Research

You’re testing a new statistical hypothesis on a large, unstructured dataset. You’ll need advanced visualisation options and reproducible outputs.

Consider: Analytical power, Flexibility, Customisation, Reproducibility

Scenario 3: Public Communication

You’re writing a blog post for a general audience and want to include clear, engaging, and interactive visualisations to support your story.

Consider: Accessibility, Aesthetics, Interactivity, Ease of sharing

Scenario 4 (Advanced): Multi-Stakeholder Project

You’re part of a multi-disciplinary team combining business analysts, data scientists, and executives. You need a flexible visualisation solution that allows collaboration, layered access, and dynamic updates.

Consider: Collaboration, Layered insights, Real-time updating, Security

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Visualisation Toolkits - Scenario Overview & Reference

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This activity is still TBD

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Complete some short-form research to explore other cases and impacts of poor management of data, as well as the incorporation. Post your research below (recommended in a file such as word/pdf etc.)

Reflect on these cases and critically analyse the following:

1. Causes: What were the root causes of the data issues in each case? Consider whether they were due to human error, system limitations, or inadequate oversight.

2. Situational Differences: How did the context of each organisation (e.g., industry, data types) influence the nature and impact of the data issues?

3. Impacts on Business: Compare the short-term and long-term effects on each organisation’s operations, financial standing, and reputation.

4. Preventative Measures: What data management practices could have prevented these issues? Discuss the role of data validation, system upgrades, and organisational policies.