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Data Science

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Data Analysis

Product Data Analysis: Getting Started Right Away with Claude Code

Instead of just "studying" SQL or Python, you will learn how to ask the essential questions needed for your service/business growth right now and immediately extract everything from analysis to insights and execution strategies. While working as a tech lead and taking on the role of a PO, I found a need for growth data analysis, and by applying this method to actual work, I significantly improved the speed of decision-making and execution. In this course, I have organized everything—from question templates used in the field to deriving actions through funnel, conversion, and churn analysis—so that you can apply it just by following along.

1 learners are taking this course

Level Beginner

Course period Unlimited

  • macro
Growth Hacking
Growth Hacking
Performance Marketing
Performance Marketing
Service Planning
Service Planning
Data literacy
Data literacy
claude
claude
Growth Hacking
Growth Hacking
Performance Marketing
Performance Marketing
Service Planning
Service Planning
Data literacy
Data literacy
claude
claude

What you will gain after the course

  • You can identify conversion/churn issues based on your own service data and derive actionable strategies.

  • You can create a professional routine that extracts analysis, insights, and actions within one minute using just a single-line question.

  • You can extract the necessary data yourself without depending on the development team, allowing for rapid experiment and improvement cycles.

Is this the situation you're in?

I want to see the data,
but when I request an analysis, it takes several days,

When you ask because it's urgent:

"I'm currently working on another analysis first."
"I think it will be difficult this week."

So in the end… I might as well just do it myself!

It has probably happened to you at least once.


But if you think about it, it's a bit strange.

The data is already there,
so why do the analysis results always take so long?

Usually, the reason is simple.

  • There is a shortage of analytical personnel, or

  • Or priorities get pushed back

  • The process of request → understanding → analysis → reporting is too long.

And even if the analysis results do come out…

👉 It's not often that things are organized all the way down to actionable steps.


So, let me ask you a question.

What if this were the situation?

I suddenly got curious at 11 PM and asked a question,
and the data analysis started immediately,

👉 Insights generated based on current data
👉 Execution strategies organized together
👉 Ready for execution the very next day.

To be honest…

Doesn't it feel like your work speed will be completely different?


This course is exactly about creating that.

Rather than a course for learning data analysis,

We will show you how to create an AI data analyst exclusively for your company.

To put it simply:

👉 A structure that analyzes immediately upon questioning,
👉 organizes insights,
👉 and even extracts actionable steps.

It feels like having a data analyst who works 24/7, 365 days a year without resting.


The reason why this is important is:

Data analysis talent these days:

  • It is not easy to find them

  • And the costs are high

  • Even if they are available, they are overwhelmed with a backlog of work.

  • It is difficult to keep up with the speed.

So, many teams eventually:

👉 You have data but can't utilize it
👉 You miss the timing while waiting for analysis
👉 You end up going back to making decisions based on gut feeling.

But in environments where execution speed is critical,
this makes a significant difference.


So, this is how we approach it.

Using Claude Code:

✔ Directly connect our company data
✔ Create an automated analysis flow when a question is asked
✔ Build a structure for deriving insights + execution strategies.

It's not just a tool explanation:

👉 How to build an analyst structure that works in actual business
👉 Focused on methods that can be applied to practical work immediately.


It might feel difficult.

So, I have structured the lecture as follows:

👉 Practical question templates provided
👉 Hands-on practice environment based on real databases
👉 Includes connection / permissions / security
👉 Explanation of workflows for immediate company application.

After listening, people usually say:

"Oh, I should use this right away."


In particular, these types of people look for it often.

  • PM / PO / Service Planner

  • Performance Marketer

  • Startup operator

  • An organization without a data team.

They have one thing in common.

👉 An environment where you must view quickly,
👉 decide quickly,
👉 and execute immediately.


I would like you to consider just one last thing.

What is needed right now:

Is it studying data analysis?

Or...

Is it a structure where you can analyze at any time and move straight to execution?

Because that difference could ultimately
determine the speed of service growth.


A Story from the Creator (Current Tech Lead / PO)

I was the same way at first.

Data was available, but the execution speed was slow;
analysis was conducted, but it did not lead to decision-making.

So the method I created is:

👉 Question-oriented analysis
👉 A structure that leads directly to action
👉 Template-based analysis tailored to practical work

While applying this to actual work:

  • Improving decision-making speed

  • Increased experiment execution speed

  • Establishing a data-driven growth system

I have experienced the effects.

This course is a direct compilation of that process.



Recommended for
these people

Who is this course right for?

  • Startup CEOs or solo operators who aren't seeing sales growth but don't know where the problem lies.

  • Marketers and growth managers who feel self-conscious about having to make requests to the developer or data team every time.

  • Service operators who have low sign-up and conversion rates but have never used data to identify the cause.

  • Analysts or beginners who want to extract metrics from a database and make decisions immediately, without needing SQL or Python.

  • PMs/POs who need to conduct funnel (AARRR) analysis but don't know where to start

  • A manager who is running ads but doesn't know what the problem is because sales aren't increasing.

  • Those who wish to get a job or change careers in product marketing or growth marketing

Need to know before starting?

  • Someone who knows how to sign up and install programs.

Hello
This is

We share technical know-how used in the field.

Curriculum

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16 lectures ∙ (34min)

Course Materials:

Lecture resources
Published: 
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