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AI for Data Analysts

IntermediateSkill Level
Updated 06/2026
Use AI across every stage of your data analysis. Write sharper prompts, audit data quality, find insights worth chasing, and ship work you can trust.
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TheoryArtificial Intelligence
4 hr
12 videos
39 Exercises
2,150 XP
Statement of Accomplishment

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Course Description

Your Practical Guide to AI-Augmented Data Analysis

AI is changing how data analysts work, and this course shows you how to use it well. You'll learn to embed an AI assistant into every stage of your analysis workflow, from interrogating raw data to delivering insights leadership will act on. DataCamp provides a built-in AI Data Assistant so you can practice on real datasets from the very first lesson. No technical background or external AI subscription required.

Write Prompts That Get Defensible Analysis

Vague prompts produce vague output. This course teaches the GCSE framework (Goal, Context, Scope, Example) for turning open-ended business questions into precise instructions an AI can act on. You'll practice on realistic scenarios across a coffee chain, a SaaS support desk, and a retail buyer's office, and learn how to spot the AI risks that hide inside polished-looking responses: probabilistic variation, hallucination, sycophancy, and missing context.

Audit Data Quality, Enrich Fields, and Find Insights Worth Chasing

Most AI demos skip the messy middle. This course doesn't. You'll work through the analyst loop on real datasets: interrogate data for fuzzy duplicates, impossible timestamps, and missing values; enrich raw fields by using AI as both doer (executing the work) and advisor (deciding what's worth doing in the first place); then surface insights across trends, distributions, differences, and outliers. Every finding gets pressure-tested before it reaches a stakeholder.

Tell Stories That Land, Then Verify Before They Ship

A dashboard or one-paragraph story is only as good as the verification behind it. You'll learn to compress dashboard discovery and prototyping from weeks to an afternoon, tailor data stories to the audience and the decision in front of you, and apply the S.P.O.T. framework (Sample-and-trace, Peer-review, Order-of-magnitude check, Test-boundaries) to catch polished-but-wrong output before it reaches leadership. The capstone runs a complete AI-first analysis on a US retail chain, then closes with a bonus lesson from the Snowflake team on Snowflake Cortex.

By the time you complete this course, you'll have a repeatable framework for using AI across every stage of analysis, from prompt to dashboard to written recommendation, and the judgment to know when to trust the result, when to verify, and when to push back.

What you'll learn

  • Apply the GCSE prompting framework (Goal, Context, Scope, Example) to turn vague business questions into prompts that produce defensible analysis.
  • Interrogate data quality with AI by surfacing fuzzy duplicates, impossible timestamps, and business-rule violations before any analysis runs.
  • Surface insights worth chasing across trends, distributions, differences, and outliers, then verify each finding using calculation, mechanism, stability, and domain-knowledge checks.
  • Prototype dashboards in an afternoon and tailor data stories to the audience and decision in front of you.
  • Verify AI output with the S.P.O.T. framework (Sample-and-trace, Peer-review, Order-of-magnitude, Test-boundaries) to catch polished-but-wrong findings before they reach leadership.

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Prerequisites

There are no prerequisites for this course
1

Augmenting Data Analysis with AI

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.
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2

Exploring Data and Developing Insights

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.
Start Chapter
3

Visual Storytelling and Acting on Insights

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.
Start Chapter
4

Capstone Project: A Complete AI-First Analysis

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.
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FAQs

Is this course suitable for beginners?

Yes. The course assumes basic familiarity with reading data (tables, columns, simple comparisons) but no prior experience with AI tools is required. It starts from first principles, showing where AI fits across the five-stage analysis cycle and how to write prompts that produce defensible analysis.

What topics does this course cover?

You will learn the GCSE prompting framework (Goal, Context, Scope, Example) for turning vague asks into actionable instructions, how to connect AI to your data via flat files, MCP, or a governed semantic layer, how to interrogate data quality and enrich fields with AI as both doer and advisor, how to surface and verify insights across trends, distributions, differences, and outliers, how to build dashboards through discovery and prototyping, and how to apply the S.P.O.T. verification framework to catch polished-but-wrong AI output before it reaches a stakeholder.

Do I need a Claude or ChatGPT subscription to take this course?

No. DataCamp provides a built-in AI Data Assistant inside every exercise, so you can practice on real datasets from the first lesson without any external AI tool subscription or account.

What datasets and scenarios will I work with?

You will work through realistic business scenarios at a four-store coffee chain (The Daily Grind), a SaaS support desk (Helios), a multi-store retailer (Iron and Grain), and a US retail chain (Board and Beyond). The capstone project runs a complete AI-first analysis on Board and Beyond, closing with a one-paragraph story tailored to the CEO.

How does this course help experienced analysts, not just those new to AI?

It goes beyond basic prompting into the harder questions experienced analysts actually face: when to trust an AI finding versus verify it, how to use AI as an advisor on what's worth enriching rather than only a doer that executes, how to spot sycophancy and probabilistic variation in polished output, and how to tailor data stories to the audience and decision in front of you. The course also covers production-grade data connections with MCP and semantic layers.

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