The AI-Assisted Analysis Mindset

The Data Analysis Problem

Here’s what usually happens with data:

Someone exports a spreadsheet. It has 50 columns and 10,000 rows. They stare at it. They sort a few columns. They make a bar chart of something. They write “sales increased” in a report.

That’s not analysis. That’s describing what you see.

Real analysis answers questions: Why did sales increase? Which customers drove the growth? Is this sustainable? What should we do differently?

Most people don’t get to these questions because the mechanics take too long. By the time they’ve cleaned the data and made the charts, they’re out of time for thinking.

What to Expect

This course is broken into focused, practical lessons. Each one builds on the last, with hands-on exercises and quizzes to lock in what you learn. You can work through the whole course in one sitting or tackle a lesson a day.

How AI Changes the Game

AI doesn’t make you smarter about data. But it removes the friction that prevents good analysis:

The time you save on mechanics becomes time for thinking.

The Analysis Workflow

Good data analysis follows a consistent pattern:

QUESTION → EXPLORE → ANALYZE → VISUALIZE → INTERPRET → COMMUNICATE
    ↑                                                        │
    └──────────────── Refine based on findings ──────────────┘

Question: What are we trying to understand? Explore: What’s in this data? What’s the shape? Analyze: What patterns exist? What’s unusual? Visualize: How can we see this clearly? Interpret: What does this mean for our situation? Communicate: How do we share findings effectively?

AI helps at every stage except interpretation—that requires your human judgment.

Setting Up Your AI Analysis Workflow

You’ll need:

1. An AI assistant Claude, ChatGPT, or similar. For data work, choose one that can:

• Process structured data (CSV, tables)
• Generate code if needed
• Create or describe visualizations

2. A spreadsheet program Excel, Google Sheets, or similar. This is where data lives and often where you’ll work with it.

3. Data to analyze Your own business data, or practice datasets. We’ll provide some in this course.

4. A clear question The most important input. AI can help explore, but you need to know what you’re looking for.

What AI Can and Can’t Do

AI Excels At:

Data exploration “Summarize this dataset. What are the columns? What’s the range of values?”

Calculations “Calculate the month-over-month growth rate for each region.”

Pattern identification “Are there any unusual values or outliers in this data?”

Visualization suggestions “What’s the best chart type to show this relationship?”

Explanation “What does this correlation coefficient mean in plain English?”

AI Needs Your Help With:

Context AI doesn’t know your business, customers, or situation.

Judgment Is this finding meaningful or just noise?

Action What should we actually do based on these findings?

Validation Are these results correct? Do they match other sources?

Quick Win: Your First AI Exploration

Let’s try something immediately useful.

If you have data, copy a sample (first 50-100 rows) into your AI assistant with this prompt:

Here's a sample of my data:

[Paste your data]

Please tell me:
1. What columns are in this data?
2. What's the data type of each column?
3. Are there any obvious issues (missing values, inconsistencies)?
4. What questions could this data potentially answer?

In 30 seconds, you have a data profile that might have taken 30 minutes to compile manually.

What You’ll Learn

Over 8 lessons:

Key Takeaways

• Data analysis fails when we spend all our time on mechanics and none on thinking
• AI accelerates the mechanics: exploration, calculation, visualization
• You provide what AI can’t: context, judgment, and action decisions
• The analysis workflow: Question → Explore → Analyze → Visualize → Interpret → Communicate
• Good analysis starts with a clear question

Next: how to ask questions that actually lead to insights.

Up next: In the next lesson, we’ll dive into Asking Better Questions.