Day-1 of AI learning · Codebasics AI Community

Pratima Verma

1d (edited) • 🏆 Wins & Progress

Day-1 of AI learning

Learned very basic what and Whys of AI:

What are models and what they contain?

What is done during training phase of a model?

What is called Inference ?

What is generative AI?

What is Agentic AI?

What are LLMs ? Different types of LLMs?

What is Machine Learning ? Types of it?

Got the feeling of reopening my neural networks which were built during studying a college course of DATA Mining. :)

One question asked to chatgpt:

Question: Tell me about Data Analyst and Data Engineers work like I'm five.

Ans:

Imagine you're 5 years old. 😊

You are building with LEGO.

On the floor, you have 10,000 LEGO pieces mixed together.

To build a castle, what do you do?

First, you look at the pieces. 👀

Then you sort them by color and size. 🟦🟥🟨

Then you follow instructions. 📖

Finally, you build the castle. 🏰

Being a Data Analyst or Data Engineer is almost the same.

Data is just like LEGO pieces

Imagine a company sells toys.

Every day, they collect information like:

Someone bought a teddy bear.

Someone returned a toy car.

Someone paid ₹500.

Someone visited the website.

After one year, they have millions of these little pieces of information.

It's a huge messy LEGO box.

What does a Data Engineer do?

Think of a Data Engineer as the person who cleans the room before anyone plays.

They say:

"Let's put all red LEGO together."

"Let's remove broken pieces."

"Let's keep everything in the right boxes."

In the data world, this means:

Collecting data

Cleaning data

Moving data

Storing data safely

Making sure it arrives quickly

Without them...

❌ The data is messy.

What does a Data Analyst do?

Now the room is clean.

The Data Analyst comes in and asks:

"Hmm... what can these LEGO pieces tell us?"

For example:

Which toy sold the most?

Which city bought the most toys?

Why did sales drop this month?

Which customers spend the most?

Then they make beautiful charts so everyone understands.

So why is learning important?

Imagine you want to become the world's best LEGO builder.

Would watching one YouTube video be enough?

No.

You build...

🏠 a house

🚗 a car

🚀 a rocket

🏰 a castle

Each time you build something, your brain learns.

Learning data is exactly like that.

Every skill is a LEGO brick

Suppose today you learn Excel.

Now you have one brick.

Tomorrow you learn SQL.

Another brick.

Then Power BI.

Another brick.

Then Python.

Another brick.

Then Statistics.

Another brick.

Then Business Thinking.

Another brick.

One brick doesn't look impressive.

But after months...

You suddenly have a huge castle.

Why practicing matters

Imagine learning to ride a bicycle.

Could you become good by only reading a book?

No.

You have to:

🚲 fall

🚲 get up

🚲 try again

Data is exactly the same.

Every dashboard you build...

Every SQL query you write...

Every DAX formula you debug...

...makes your brain stronger.

What happens inside your brain?

Your brain is like a forest.

The first time you learn CALCULATE() in Power BI...

It's like walking through a jungle.

🌳🌳🌳🌳🌳

No path exists.

The second time...

A tiny path appears.

The tenth time...

A road appears.

The hundredth time...

It's a highway.

That's why experienced analysts can solve problems quickly—they've built many "highways" through practice.

Why projects are so powerful

Imagine someone says:

"Build a toy shop using LEGO."

Now you use:

sorting

counting

building

decorating

all together.

A data project works the same way.

One project makes you use:

Excel

SQL

Power BI

DAX

Data cleaning

Charts

Business thinking

All at once.

That's why projects teach more than simply watching videos.

The secret that many beginners miss

Many people think:

"If I finish 100 courses, I'll become a Data Analyst."

But companies don't pay you for the number of courses you've watched.

They pay you because you can solve problems.

For example:

"Why did sales drop?"

"Which product should we stop selling?"

"Which customers are most valuable?"

Your tools help you answer these questions.

Think of your learning journey like this

Learn 📚

↓

Practice ✍️

↓

Make Mistakes ❌

↓

Fix Mistakes 🔧

↓

Build Projects 📊

↓

Gain Confidence 💪

↓

Get Better Opportunities 🚀

The biggest lesson

Learning isn't about collecting certificates.

It's about training your brain to:

Notice patterns in data.

Ask good questions.

Solve business problems.

Explain insights clearly to others.

Over time, you'll stop memorizing formulas and start thinking like a data professional. That's when you become a strong Data Analyst—and if you also learn how to build reliable data pipelines and systems, you can grow into a skilled Data Engineer as well.

2 comments

Day-1 of AI learning