The AI-augmented analyst

1. The AI-augmented analyst

Welcome to the course!

2. Meet your instructor...

I'm Andy Cotgreave, co-author of The Big Book of Dashboards and Dashboards That Deliver with over twenty years of experience in data at companies like Tableau. Together, we'll explore how AI can augment your work as a data analyst.

3. The journey ahead

By the end of this course, you'll know how to use AI throughout the data analysis cycle. In the videos, I'll be working primarily with Gemini and Claude, but the principles you're going to learn apply to any AI assistant-powered analytics tool you choose.

4. The journey ahead

And in the exercises, you'll be working with DataCamp's own AI analytics tool.

5. The data analysis workflow

You already know the data analysis cycle — you start with a task or request, connect to data and clean it, find insights, and build the outputs that drive decisions. And then it repeats: good insights always lead to more questions.

6. The data analysis workflow, augmented

What's changed is that AI assistants are now in the middle of that cycle with you. At every stage, they can help you work faster, explore further, and communicate more clearly - when done with care. Let's look at what all of that actually means.

7. Natural language data exploration

One of the biggest shifts AI brings is this: you can now explore data through conversation. Instead of starting with a blank screen or writing SQL queries from scratch, you ask your dataset "which regions are growing fastest?" and get an answer in seconds. Follow up, drill down, test a hypothesis — all in plain language. You spend less time getting to the data and more time thinking about what it means.

8. Understand and clean your data

When you connect to a new dataset, you often know very little about it. Gen AI can infer meaning, describe your fields, and spot anomalies, giving you a solid starting point. It won’t be as robust as a strong data dictionary or semantic layer, but it’s a good start. We’ll cover how to add better context later in the course

9. Understand and clean your data

And data is rarely clean. AI can find and help fix multiple spellings, inconsistent formats, duplicates, and rogue entries that would otherwise slow you down.

10. Find insights

Gen AI can analyze your data for trends, distributions, outliers, and correlations, and suggest useful aggregations. It can also bring its understanding of the world to your data, helping you spot causes for patterns you might otherwise have missed.

11. Build and share: dashboards, apps, and stories

AI can help you build analytical assets faster — describe a dashboard and AI can suggest layouts, chart types, and write the code. It's also very good at helping you tell the stories in your data.

12. Build and share: dashboards, apps, and stories

Here's a chart from Our World in Data — showing how Americans spend their time throughout their lives. Neutral and informative in its default state, right? But with small changes —

13. Build and share: dashboards, apps, and stories

color highlights, a reframed title — the same data tells a completely different story; these examples were built by Claude. AI can help you find and draft narrative angles, and turn your findings into executive summaries and reports that people will actually read.

14. The AI application landscape

The landscape of AI in data analytics is rapidly evolving. AI assistants like ChatGPT, Claude, and Gemini offer flexibility with minimal setup. BI platforms like Tableau and Power BI are building their own copilots.

15. The AI application landscape

At the data layer, integrated tools like Snowflake Cortex can extract insights from your data with natural language.

16. The AI application landscape

And there's a new breed of startups experimenting with AI-first approaches throughout the entire analytics stack. Throughout the course, the principles you learn here will be applicable to any AI tool you use in your work.

17. Challenges... and how we'll tackle them

You must remember: AI assistants aren't perfect. They're gonna help you a huge amount, but there are real risks. Gen AI is prone to hallucinations — and those can leak into calculations or data stories the AI builds for you.

18. Challenges... and how we'll tackle them

It's a probabilistic technology, so you can't guarantee the same results from identical prompts.

19. Challenges... and how we'll tackle them

And it generally lacks the context behind your data and your intent — although integrations that connect AI directly to your organization's data and software are changing that fast. Throughout this course, we'll cover the prompting techniques and tool integrations that help you get the most out of AI while keeping your work reliable.

20. Let's practice!

Time to begin augmenting your analyses with AI!

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