Capstone Project: Board and Beyond

1. Capstone Project: Board and Beyond

Welcome to the Capstone Project! You've covered the whole data analysis workflow, connecting, cleaning, finding insights, and communicating them. In this final chapter, you’re going to tackle a full end-to-end analysis by applying all of the techniques you’ve learned.

2. Board and Beyond

You'll be working as the data analyst for Board and Beyond, a board game retail chain with locations across the USA.

3. Board and Beyond: The Data

The dataset covers a full year of transactions across five product categories: strategy games, card games, miniatures, puzzles, and RPG supplies.

4. Board and Beyond: The Data

You'll also have a subset of customer reviews.

5. Board and Beyond: The Goal

Your goal is as follows: Get to know the structure of your data. Clean and enrich it ready for analysis. Turn user stories into actionable dashboards. And finally, build data stories your colleagues can use to drive change in the organization.

6. Let's practice!

Have fun!

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This exercise is part of the course

AI for Data Analysts

IntermediateSkill Level

4.8+

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Set up your AI-augmented analyst toolkit. Learn where AI fits across the five-stage analysis cycle, master the GCSE prompting framework for turning vague asks into actionable recommendations, and choose the right way to connect AI to your data: flat files, MCP, or a governed semantic layer.

Exercise 1: The AI-augmented analyst Exercise 2: Get to know The Daily Grind Exercise 3: Same prompt, different answer Exercise 4: Writing prompts that work Exercise 5: Pick a prompt apart with GCSE Exercise 6: Vague vs. structured prompt Exercise 7: Connecting to your data Exercise 8: Flat file, MCP, or semantic layer?Exercise 9: Context unlocks meaning

Move from raw data to insights you can defend. Interrogate data quality for fuzzy duplicates, missing values, and impossible timestamps; enrich raw fields with AI as both doer and advisor; then find insights worth chasing across trends, distributions, differences, and outliers, and verify each one before it reaches a stakeholder.

Exercise 1: The interrogation phase: data quality Exercise 2: What can AI flag on its own?Exercise 3: Interrogate the coffee shop data Exercise 4: Interrogate the AI's deduplication Exercise 5: Enriching your data Exercise 6: Extract intents from support tickets Exercise 7: Doer or advisor?Exercise 8: Finding insights you can trust Exercise 9: Where would you start exploring?Exercise 10: Drill into a finding

Turn findings into dashboards and stories that land. Compress dashboard discovery and prototyping from weeks to an afternoon, tailor data stories to the audience and decision in front of you, and protect against polished-but-wrong output with the S.P.O.T. verification framework.

Exercise 1: Building dashboards with AI Exercise 2: From interviews to user stories Exercise 3: Prototype and iterate Exercise 4: Telling data stories with AI Exercise 5: Same data, two stories Exercise 6: Honest framing vs. misleading Exercise 7: Story for the CFO Exercise 8: Verifiability and trust Exercise 9: Which S.P.O.T. check catches this?Exercise 10: Does this number feel right?Exercise 11: Spot the polished-but-wrong dashboard

Run a complete AI-first analysis on Board and Beyond, a US retail chain. Audit data quality, identify the enrichments a category manager would actually use, surface and verify a headline finding, build a dashboard that backs an expansion decision, and deliver a one-paragraph story to leadership.

Exercise 1: Capstone Project: Board and Beyond

Current Exercise

Exercise 2: Get to know Board and Beyond Exercise 3: Which mess can't wait?Exercise 4: What would a category manager actually use?Exercise 5: Verify a headline finding Exercise 6: Where should we expand?Exercise 7: Tell the story to leadership Exercise 8: Congratulations!Exercise 9: Bonus: Data Analysis with Snowflake Cortex