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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 you'll 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

What we'll cover

  1. What you're looking at

    1. What is exploratory data analysis, and what do I do first with a new table?reference
    2. What does a row in a table represent, and how do I check?
    3. How do I check a table's rows, columns and data types in pandas?
  2. What's missing

    1. How do I count the missing values in each column with pandas?
    2. Why is data missing from a table, and what changes in my analysis?
  3. One column at a time

    1. How do I draw and read a histogram of a numeric column?
    2. How do I read describe() for a column: mean versus median?
    3. How do I check a column's categories with value_counts()?
    4. Is an extreme value an error or real data?
  4. Relationships and your log

    1. How do I look at the relationship between two columns without claiming a cause?
    2. How do I do a complete first look at a table and write it down?
    3. What should I check in the first 30 minutes with a new table?

Common questions

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 doesn't teach Python from scratch: the code in each exercise is short and already written; you change it and read what comes back.
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.

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 one question per page, on up to five pages a day.

  • 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've worked with data for about ten years, mostly at mission-driven organizations: policy research first, then education, and now early childhood services. 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