Getting Started on Your First Coding/Data Project

So you’ve been dabbling in coding and you’re ready to make it feel a bit more real—like you’re actually building something. But how do you take that leap from tutorials to your first proper project?

Shift Your Mindset: It’s Not Just About Showing Off

People often say, “Build a portfolio of projects to show employers!” And that’s valid advice. But if you’re a perfectionist like me, aiming to impress others can sometimes make you freeze up. You end up with 12 half-finished projects and a creeping sense of inadequacy.

So here’s my take: don’t build a portfolio for others—build one for yourself.

Let your projects document your learning. Let them remind you, “I can do this.”

What Even Is a Data Project?

That depends on your interests, but a simple data project usually has four main steps:

1. Find Some Data

Good news: data is everywhere.

• Try the Office for National Statistics for well-structured public data.
• Or, if you’re feeling more adventurous, Kaggle is an incredible resource for quirky, fun, and beginner-friendly datasets. For example, this collection of fun datasets includes everything from avocado prices to shoe reviews. I found one on women’s shoes—so let’s use that.

Once you’ve downloaded your CSV, open it in Excel. Yep, Excel. It’s a fast way to take a first look:

• What columns are there?
• How many rows?
• Is the dataset long (many rows) or wide (many columns)?
• Does anything look messy?

This visual check only takes 30 seconds and helps you start forming questions about the data.

2. Load It Into Python

Now let’s get it into your code environment. I recommend using Google Colab for this—it’s fast, free, and doesn’t require setup.

import pandas as pd
data = pd.read_csv('data_womens_shoes.csv') # needs to match your filename
data.shape  # shows number of rows and columns
data.columns  # lists column names
data.head()  # shows the first 5 rows
data.info()  # info on data types and missing values
data.describe()  # summary stats for numeric columns 

Questions to ask:

• Are there missing values?
• Which columns look useful?
• What story might this data tell?

This exploration phase is crucial. It’s where ideas for analysis or visuals often emerge.

4. Do Something With It

Now you decide where your project goes:

• Want to analyse trends? Try grouping by product type or price range and calculating averages.
• Want to visualise it? Use matplotlib or seaborn to create bar charts, scatter plots, or histograms.
• Want to practice data cleaning? Try handling missing values, renaming columns, or filtering rows.

Here’s a simple visualisation example:

import seaborn as sns
import matplotlib.pyplot as plt

sns.histplot(data['prices.amountMax'], bins=20)
plt.title('Distribution of Max Shoe Prices')
plt.xlabel('Price')
plt.ylabel('Count')
plt.show()

And if you’re feeling brave, you can save your cleaned or summarised dataset with:

data.to_csv('cleaned_shoes_data.csv', index=False)

Exporting it can be a nice final step—especially if you want to revisit it later or include it in a portfolio.

Final Thoughts

The first project isn’t about perfection. It’s about momentum.

Let yourself play. Let the questions guide you. Your only goal? Finish something—even something small.

Then celebrate it. You’ve taken a real step from “learning to code” to “being a coder.”

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👟 P.S. If you did end up exploring that shoe dataset, I’d love to hear what you discovered. Tag me or share it with a friend—let’s keep building. 💙