Exploratory Data Analysis: Your First 30 Minutes with a New Dataset
Open any new table and in half an hour know what you have, what to trust and what to ask before you calculate anything.
You go at your own pace, with your Maestro beside you, 1:1, through the whole course: ask it anything, and it asks you questions too.
14 skills · 4 modules
What will you be able to do?
Each box is a skill. The base is at the bottom, recognizing and explaining, and you work your way up to building something of your own and judging what works and why. Hover over a box to see what it builds on. Click it to see where I teach it and how your Maestro checks it.
- Synthesize
- Analyze
- Apply
- Understand
- Remember
Synthesize
Keep a log of findings and questions
Where I teach it: Relationships and your log · How do I do a complete first look at a table and write it down?
Builds on:
- Find out why data is missing and where it clusters
- Tell an error from a real extreme value
- Interpret the relationship between two columns without claiming a cause
Your Maestro asks you to build something of your own with it and saves it as evidence when you do.
Do a complete first look at a new table
Where I teach it: Relationships and your log · How do I do a complete first look at a table and write it down?
Builds on:
- Keep a log of findings and questions
- Name the steps of a first look at a table
Your Maestro asks you to build something of your own with it and saves it as evidence when you do.
Analyze
Find out why data is missing and where it clusters
Where I teach it: What's missing · Why is data missing from a table, and what changes in my analysis?
Builds on:
- Count the missing values in each column
Your Maestro asks you to break a problem down with it and saves it as evidence when you do.
Tell an error from a real extreme value
Where I teach it: One column at a time · Is an extreme value an error or real data?
Builds on:
- Summarize a numeric column with describe(), median and mean
- Draw a histogram and adjust its bins
Your Maestro asks you to break a problem down with it and saves it as evidence when you do.
Interpret the relationship between two columns without claiming a cause
Where I teach it: Relationships and your log · How do I look at the relationship between two columns without claiming a cause?
Builds on:
- Plot two columns at once
Your Maestro asks you to break a problem down with it and saves it as evidence when you do.
Apply
Check a table's shape and types
Where I teach it: What you're looking at · How do I check a table's rows, columns and data types in pandas?
Builds on:
- Say what one row of a table represents
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Count the missing values in each column
Where I teach it: What's missing · How do I count the missing values in each column with pandas?
Builds on:
- Check a table's shape and types
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Draw a histogram and adjust its bins
Where I teach it: One column at a time · How do I draw and read a histogram of a numeric column?
Builds on:
- Read the shape of a column in a histogram
- Check a table's shape and types
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Summarize a numeric column with describe(), median and mean
Where I teach it: One column at a time · How do I read describe() for a column: mean versus median?
Builds on:
- Check a table's shape and types
- Read the shape of a column in a histogram
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Count a column's categories and spot stray labels
Where I teach it: One column at a time · How do I check a column's categories with value_counts()?
Builds on:
- Check a table's shape and types
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Plot two columns at once
Where I teach it: Relationships and your log · How do I look at the relationship between two columns without claiming a cause?
Builds on:
- Draw a histogram and adjust its bins
- Count a column's categories and spot stray labels
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Understand
Say what one row of a table represents
Where I teach it: What you're looking at · What does a row in a table represent, and how do I check?
Your Maestro asks you to explain it in your own words and saves it as evidence when you do.
Read the shape of a column in a histogram
Where I teach it: One column at a time · How do I draw and read a histogram of a numeric column?
Your Maestro asks you to explain it in your own words and saves it as evidence when you do.
Remember
Name the steps of a first look at a table
Where I teach it: Relationships and your log · What should I check in the first 30 minutes with a new table?
Your Maestro asks you to recognize and recall it and saves it as evidence when you do.
Who is this course for?
For anyone who receives a new table and wants to know what they have, what to trust, and what to ask before calculating anything. You need to be able to run a cell and change a line of code, with help from your Maestro; the course does not teach Python from scratch. Here you find and log the problems; cleaning them is covered in another course.
Ask your Maestro
Your Maestro is an AI that knows this course. Ask it about the first page and it answers right here, free.
You: Where do I start?
Your Maestro: From what you already know. Tell me what you've done with data, and I'll start this page from there.
Asking about: Answers from the page "What is exploratory data analysis, and what do I do first with a new table?"
Only this browser keeps what you ask here. How it works
What we'll cover
What you're looking at
What's missing
One column at a time
Relationships and your log
How long does it take?
1 to 1.5 hours
A guide: you go at your own pace, and the course waits where you left off.
Common questions
- What do I check first when I get a new table?
- Before calculating anything, check what a row is, how many rows and columns there are, what type each column has and how many values are missing. Then look at each column on its own and note anything odd. In the course you do this in your first 30 minutes and leave a log of what you found.
- What is exploratory data analysis?
- It is the step of getting to know a table before analyzing it: its shape, what is missing, how each column is distributed and how two columns relate. Its goal is to know what to trust and what to ask before calculating. It does not clean the data yet: it finds and notes the problems.
- Do I need to know Python?
- You need to be able to run a cell and change a line of code, with help from your Maestro when you get stuck. The course does not teach Python from scratch: the code in each exercise is short and already written. You change it and read what it answers. It all runs on the page, with nothing to install.
- Do I have to install anything?
- No. You write the exercises on the course page and they run in an online practice space once you sign in to the course with your account. If you want to repeat the method on your own computer later, the last exercise downloads as a small project.
- Is the data real?
- No. Rueda, the bike-share system in the course, and its trips are made up (synthetic data, CC0). They're built to look like a real table from work, with the same problems you'll find in yours.
- Will I learn to clean data here?
- Here you learn to find and write down the problems, which is the first step. Fixing them is a different job, with its own decisions, and another course covers it.
- Do I need a course, or can I learn this on my own?
- You can learn it on your own, and many people do. A course gives you the order: what to practice first, ready-to-use data, and exercises that show where you go wrong before it happens at work. This course is free, and your Maestro answers questions as you go. If you prefer going solo, the method still works.
How do I check what AI does? I teach it in Your AI and Your Data: Don't Let It Make Up the Number and Check what AI gives you in three steps.
What you get
- 4 modules and 12 pages, open whenever you want them.
Open to everyone: read all of it, and your Maestro, an AI, answers your questions on every page.
With a free account: your Maestro for the whole course, always on the page you're on, and the exercises run with nothing to install.
Words you'll meet
In the lessons, tap one to see what it means, or ask your Maestro about it.
Who's teaching you
Sergio Sánchez
I'm Sergio, chekos online. I'm a data architect, and I've spent more than ten years working with data in the Bay Area, mostly at mission-driven organizations: policy research first, then education, and now early childhood services. I grew up in Tijuana. In 2018 I started tacosdedatos to share what I was learning, and since then it's been a blog, a podcast, courses, guides, and a community. These days I spend a lot of my time building with AI agents and writing about how I organize my work around them. I share what I've learned; you take what's useful and make it yours.
See you in the course
Exploratory Data Analysis: Your First 30 Minutes with a New Dataset