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Intro to SQL

by Rachel Tatman · Kaggle

4.5
(10,000 reviews)
500K+ enrolled3 hoursUpdated 2024-03
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Our Verdict

Worth taking

Take Kaggle's free Intro to SQL: for an absolute beginner who wants the fastest hands-on on-ramp to SELECT/WHERE/GROUP BY/JOIN plus first exposure to BigQuery, it is one of the best free options available, but treat it as a strong first few hours with SQL rather than a complete SQL education. It is a six-lesson, roughly-3-hour micro-course (authored by Rachael Tatman and Alexis Cook) that teaches core SQL by querying real Google BigQuery datasets in interactive notebooks, and it earns broadly positive learner sentiment. Its honest trade-off is depth and transferability: one widely-shared review concludes the content is 'really surface level' next to a full Coursera/database course, and because every example runs through the BigQuery Python client you spend real effort on bigquery.Client(), dataset/table references and .to_dataframe() boilerplate rather than on a plain SQL engine. The catalog's 4.5 rating could not be independently verified during this review (Class Central and Shiksha blocked automated access), so it should be treated as approximate.

It is free, genuinely interactive on real-world data, and the only major cost is time, so for a beginner the risk/reward is excellent. Multiple independent reviewers call it an excellent starting point that builds a strong querying foundation, with the main caveat being limited depth and BigQuery-specific boilerplate rather than any quality problem.

Best for: Complete SQL beginners, aspiring data scientists/analysts already on Kaggle, and anyone who specifically wants a quick, free, hands-on introduction to Google BigQuery and the pandas-to-SQL workflow. Ideal if you learn by doing and want guided exercises with hints and full solutions.

Skip if: People who already know SQL basics, anyone who needs deep relational-database theory, indexing/performance, window functions, or advanced SQL, and learners who want vendor-neutral SQL on a standard engine (Postgres/MySQL/SQLite) rather than BigQuery's client-library boilerplate. Those preparing for SQL job interviews will need a more rigorous follow-up.

About This Course

Query BigQuery datasets using SQL covering SELECT, FROM, WHERE, GROUP BY, and JOIN operations.

What You'll Learn

Connect to and explore Google BigQuery datasets in Python (bigquery.Client(), get_dataset, list_tables, table.schema, list_rows, to_dataframe)
Write core queries with SELECT, FROM and WHERE to pull and filter columns
Aggregate and summarize data using GROUP BY, HAVING and COUNT
Sort and shape result sets with ORDER BY
Improve query readability and structure using column aliases (AS) and common table expressions (WITH)
Combine multiple tables with JOIN to answer questions that span datasets
Practice the end-to-end workflow of querying large datasets and bringing results into pandas for analysis

Curriculum

Getting Started with SQL and BigQuery

Learn the BigQuery workflow: the Client object, datasets, tables, schema inspection, and pulling sample rows into pandas.

Select, From & Where

Write your first queries to select specific columns from a table and filter rows with conditions.

Group By, Having & Count

Aggregate data into groups and filter aggregated results to answer summary questions.

Order By

Sort query results and work with dates to organize and rank output.

As & With

Use aliases and common table expressions (CTEs) to make complex queries cleaner and more readable.

Joining Data

Combine information from two tables with JOIN to answer questions that require multiple data sources.

Prerequisites

  • Basic Python and pandas familiarity (lessons read query results into DataFrames via the BigQuery client)
  • A free Kaggle account to run the interactive notebooks
  • No prior SQL or database experience required

Instructor

Rachel Tatman

Instructor · Kaggle

Pros & Cons

Pros

  • Completely free with a hands-on, interactive notebook format running real queries on real BigQuery datasets, not toy examples
  • Every exercise includes a hint and a full solution, which reviewers single out as excellent for staying unstuck and motivated
  • Very fast to complete (about 3 hours / six short lessons) and ends with a shareable certificate plus LinkedIn accomplishment
  • Doubles as a practical first introduction to Google BigQuery and the pandas + SQL data workflow, useful for aspiring data scientists
  • Logical progression from SELECT/WHERE through aggregation, sorting, CTEs and JOINs covers the true fundamentals

Cons

  • Depth is limited: multiple reviewers describe the content as 'surface level' versus a full university or Coursera SQL course
  • BigQuery Python-client boilerplate (client/dataset/table references, to_dataframe) adds friction and is not transferable to plain SQL engines like Postgres or MySQL
  • Skips advanced and interview-relevant topics (window functions, subquery depth, indexing/performance, data modeling)
  • Some learners report needing outside resources (e.g., YouTube) to fully understand clauses, and slower learners spend well over the estimated 3 hours

Alternatives To Consider

Intro to Machine Learning

Kaggle

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Machine Learning Crash Course

Google

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Machine Learning Scientist with Python

DataCamp

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Frequently Asked Questions

Is Intro to SQL free?

Yes — Intro to SQL is free to access. 100% free, like all Kaggle Learn micro-courses. No payment, no audit/paid tiers. A free Kaggle account is required, and BigQuery usage is covered within the Kaggle/BigQuery sandbox so there is no cloud bill for the exercises. A free completion certificate is issued.

Who is Intro to SQL for?

Complete SQL beginners, aspiring data scientists/analysts already on Kaggle, and anyone who specifically wants a quick, free, hands-on introduction to Google BigQuery and the pandas-to-SQL workflow. Ideal if you learn by doing and want guided exercises with hints and full solutions.

What will you learn in Intro to SQL?

Connect to and explore Google BigQuery datasets in Python (bigquery.Client(), get_dataset, list_tables, table.schema, list_rows, to_dataframe); Write core queries with SELECT, FROM and WHERE to pull and filter columns; Aggregate and summarize data using GROUP BY, HAVING and COUNT; Sort and shape result sets with ORDER BY.

What are the prerequisites for Intro to SQL?

Basic Python and pandas familiarity (lessons read query results into DataFrames via the BigQuery client); A free Kaggle account to run the interactive notebooks; No prior SQL or database experience required.

Is Intro to SQL worth it?

It is free, genuinely interactive on real-world data, and the only major cost is time, so for a beginner the risk/reward is excellent. Multiple independent reviewers call it an excellent starting point that builds a strong querying foundation, with the main caveat being limited depth and BigQuery-specific boilerplate rather than any quality problem.

How we reviewed this course

This is an independent editorial assessment by Cursarium, based on Kaggle's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.

Sources

Free
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