Interroger les données du café

Premier jour chez The Daily Grind. Les données n'ont pas été auditées depuis des mois et la finance clôture vendredi ; vous devez donc signaler toute anomalie avant que les chiffres ne remontent.

Les ventes de café non nettoyées sont déjà jointes. Utilisez l'assistant pour trouver des problèmes de qualité, et examinez avec attention ce qu'il met en avant et ce qu'il ne met pas en avant.

Lesquels des éléments suivants l'IA a-t-elle signalés comme problèmes de qualité des données ?

Cet exercice fait partie du cours

<cours>L'IA pour les data analysts</cours>

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Cet exercice fait partie du cours

<cours>L'IA pour les data analysts</cours>

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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: La phase d'interrogation : qualité des données Exercise 2: Que l'IA peut-elle signaler seule ?Exercise 3: Interroger les données du café

Exercice actuel

Exercise 4: Interroger la déduplication de l'IA Exercise 5: Enrichir vos données Exercise 6: Extraire les intentions à partir des tickets de support Exercise 7: Exécutant ou conseiller ?Exercise 8: Identifier des insights fiables Exercise 9: Par où commenceriez-vous votre exploration ?Exercise 10: Approfondir un constat

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 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