End-to-End EDA on a Real Dataset
Part of the Data Science Roadmap
In short
This is your first real portfolio piece: take a public dataset, ask sharp questions and answer them with clean code and charts. Recruiters read these notebooks to see how you think.
The practice project for this phase — take a public dataset from raw file to cleaned data to visualized findings, entirely in a single well-documented notebook.
Think of it like this
Inspecting a flat before you rent
You don't sign after seeing one room. You check every room, open taps, look for damp and note problems in a list. Then you tell your friends what's good, what's bad and whether to take it. EDA is that inspection, for a dataset.
Watch it happen
Your build plan
1/6Pick a dataset you care about and write three to five questions before opening it.
The big ideas
What you need to know
Questions first
Writing questions before exploring keeps the notebook focused instead of 50 random plots.
Profile every column
Type, missing share, unique values and range. This catches most data problems early.
Each chart earns its place
Title it with the finding, label axes, and add one line underneath saying what it shows.
Finish with a summary
Three to five bullet findings and their limits. This is what a busy reviewer actually reads.
See it in code
Walk through the code
1import pandas as pd2import matplotlib.pyplot as plt3 4df = pd.read_csv("data.csv")5print(df.shape)6print(df.dtypes)7print(df.isna().mean().sort_values(ascending=False).head())8 9num = df.select_dtypes("number")10num.hist(bins=30, figsize=(10, 6))11plt.tight_layout()12plt.show()13 14print(num.corr().round(2))1/4Load the data and check its size and column types.
A starter profile for any dataset. It shows size, types, the worst missing columns, every numeric distribution and correlations.
Save yourself the pain
Common mistakes
Oops
Dumping 40 charts with no titles or comments.
Do this instead
Keep the charts that answer your questions, each with a one-line takeaway.
Oops
Picking the Titanic or Iris dataset that everyone uses.
Do this instead
Choose something fresher and closer to your interests, like local air quality or cricket stats.
Your mission
First milestone
Quick check
Two columns have a correlation of 0.9. What can you safely say?
Go deeper
- 1Choose a public dataset (Kaggle, government open data) you're genuinely curious about.
- 2Work through load, clean, explore, visualize, and conclude, in that order.
- 3Write markdown explanations alongside your code so the notebook reads like a report.
Stuck on this topic? Ask an Insider
Get 1:1 guidance from people who've walked this exact path — on the InsideEdge app.
