# Day 15: I Learned VLOOKUP Twice — First by Building It, Then by Understanding It

When I started learning Excel, I thought the goal was to learn formulas.

`IF()`

`LEFT()`

`MID()`

`RIGHT()`

`VLOOKUP()`

But after working through several projects, I'm starting to realize something:

**Learning a formula and learning when to use a formula are two different things.**

And Day 15 gave me a very good example of that.

## 🚗 Today's Project: Car Inventory

Today I continued the **FreeCodeCamp Excel course** and worked through the **Car Inventory project**.

![](https://cdn.hashnode.com/uploads/covers/6a9eac1dc28787b9fab3d112/d4a79728-82d3-4e03-9831-6f0d96b00736.png align="center")

The project looks simple on the surface: a spreadsheet containing information about cars, drivers, mileage, manufacturing years, warranty coverage, and IDs.

But underneath that simple dataset are several important data-analysis skills.

My sheet looked something like this:

| Car ID | Make | Model | Manufacture Year | Age | Miles | Miles / Year | Driver | Covered? |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| CH2010PTC0001 | CH | PTC | 2010 | 16 | 179,772 | 11,235.75 | Davis | NOT COVERED |
| TY2012COR0002 | TY | COR | 2012 | 14 | 178,640 | 12,760.00 | Johnson | NOT COVERED |
| CH2020PTC0003 | CH | PTC | 2020 | 6 | 176,794 | 29,465.67 | Anderson | NOT COVERED |
| HY2009ELA0004 | HY | ELA | 2009 | 17 | 175,918 | 10,348.12 | Brown | NOT COVERED |
| TY2017COR0005 | TY | COR | 2017 | 9 | 169,815 | 18,868.33 | Taylor | NOT COVERED |

The interesting thing wasn't the cars.  
It was what I could **do with the data.**

# 🔎 VLOOKUP: The Part That Hit Differently

Here's where Day 15 connected with something I had already done.

On **Day 12**, I had already built a VLOOKUP solution myself.

At that point, I wasn't following a textbook workflow.

I had a problem:

> **“I have a value here, and I need Excel to find the corresponding information somewhere else.”**

So I built a solution around that requirement.

Then, on Day 15, FreeCodeCamp officially introduced VLOOKUP as part of the Car Inventory project.

And something interesting happened.

I wasn't seeing VLOOKUP for the first time.

I was seeing **the textbook version of something I had already discovered through practice.**

## 🧠 Day 12 vs Day 15

|  | **Day 12 — My Approach** | **Day 15 — FreeCodeCamp** |
| --- | --- | --- |
| Starting point | A problem I wanted to solve | A structured project |
| Approach | Build the solution myself | Learn the standard method |
| VLOOKUP | Learned through application | Learned as an Excel feature |
| Main lesson | “How can I retrieve this?” | “This is the standard tool for it.” |
| Learning style | Problem → solution | Concept → application |
| Biggest value | Discovery | Formalization |

And this distinction matters.

### **The formula didn't become more powerful on Day 15.**

**My understanding became deeper.**

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# 📊 From Raw Data to Information

![](https://cdn.hashnode.com/uploads/covers/6a9eac1dc28787b9fab3d112/4cabb5f9-9537-4aef-8745-d0c19bd6e5d5.png align="center")

The project also pushed me beyond individual formulas.

I started working with:

*   importing data
    
*   splitting cells
    
*   text functions
    
*   VLOOKUP
    
*   conditional logic
    
*   combining text
    
*   Pivot Tables
    
*   charts
    

I tracked my progress like this:

| Topic | Formula / Feature | Status |
| --- | --- | --- |
| Import text files | Import | ✅ Done |
| Split cells | `LEFT`, `MID`, `RIGHT` | ✅ Done |
| Lookup values | `VLOOKUP` | ✅ Done |
| Conditional logic | `IF` | ✅ Done |
| Combine text | `CONCATENATE` | ✅ Done |
| Summarize data | Pivot Tables | ✅ Done |
| Visualize data | Charts | ✅ Done |
| Export results | Copy to Docs | ✅ Done |

This table is probably my favorite part of the project.

Because it shows something I didn't understand when I started:

> **Data analysis isn't one skill. It's a chain of small skills that eventually work together.**

# 📈 I Didn't Want to Stop at the Formula

Once the data was structured, I started asking questions.

For example:

### How does mileage vary with vehicle age?

I created a scatter plot of:

![](https://cdn.hashnode.com/uploads/covers/6a9eac1dc28787b9fab3d112/cc25ad57-61dc-4945-910d-bb6d7e9cb4bc.png align="center")

**Age → Miles**

The result wasn't a “beautiful answer.”

And that's okay.

The important part was learning to move from:

**data → formula → analysis → visualization**

instead of stopping at:

**data → formula.**

# 👥 Then Came the Pivot Table

I also summarized total mileage by driver.

![](https://cdn.hashnode.com/uploads/covers/6a9eac1dc28787b9fab3d112/a2dc10a6-8ab5-439d-8fbd-0ef4814a93a8.png align="center")

My Pivot Table produced:

| Driver | Sum of Miles |
| --- | --- |
| Wilson | 122,666 |
| Miller | 270,618 |
| Moore | 362,543 |
| Davis | 370,802 |
| Johnson | 434,248 |
| Anderson | 441,587 |
| Brown | 564,791 |
| Taylor | 643,324 |
| Williams | 655,846 |
| **Grand Total** | **3,866,425** |

And then I turned the summary into a chart.

That small exercise taught me something important about Pivot Tables:

> **A Pivot Table isn't just a way to make a table look cleaner. It's a way to ask a question of your dataset.**

In this case:

**“How much total mileage is associated with each driver?”**

# 🧩 The Bigger Lesson

When I started this journey, I was worried about learning the “right” way.

Should I memorize formulas?

Should I follow tutorials exactly?

Should I build projects?

Should I understand every function before moving forward?

I'm slowly realizing that I need all three:

### **1\. Discover**

Try solving a problem yourself.

### **2\. Formalize**

Learn how the standard tool or method works.

### **3\. Apply**

Use it on another dataset until it becomes natural.

That's exactly what happened with VLOOKUP.

**Day 12:** I built it.

**Day 15:** I saw it taught properly.

The second experience was more valuable because the first one existed.

# 🎯 Why This Matters for My Quant Journey

Right now I'm still very early in my 10-month journey.

I'm working with spreadsheets.

Eventually, I'll move into:

**SQL → Python → Statistics → Machine Learning → Financial Data**

And eventually, the goal is to work with quantitative trading problems.

Obviously, VLOOKUP isn't going to build a trading strategy.

But that's not the point.

The point is developing the habit of asking:

> **What is the problem?**

> **What information do I have?**

> **What information do I need?**

> **How can I transform the data to get there?**

That way of thinking will matter much more when the spreadsheets become databases, the databases become financial datasets, and the formulas eventually become Python code.

# 📝 What I Took Away From Day 15

### **Technical**

*   VLOOKUP
    
*   Text manipulation
    
*   IF logic
    
*   Data importing
    
*   Pivot Tables
    
*   Charts
    
*   Basic data visualization
    

### **More importantly**

I learned that **building something yourself before learning the textbook method changes how you understand the textbook method.**

That's probably my biggest takeaway from today.

## Day 15 Status

**Excel:** 🟢  
**Data cleaning:** 🟢  
**Lookup logic:** 🟢  
**Pivot Tables:** 🟢  
**Visualization:** 🟢  
**Quant journey:** Still just getting started.

> **15 days down. A lot more to learn.**

I'm not trying to look like a Data Scientist yet.

I'm trying to become one.

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