The Analytics Education We Wish We Had When We First Started

Lifetime access to on-demand content, a community of aspiring data practitioners, and no prior experience required. Learn to work with Google Sheets, write SQL to a PostgreSQL Database powered by AWS, and build reports and dashboards in Looker.

Step-by-Step Learning Roadmap
Decode complexity, and know exactly where to start
Uncovering the world of data analytics, and decoding the complexity of tools is only the beginning.

We've spotted all the barriers, discovered how to piece together all the dimensions that matter most, and built a roadmap to help you land your first role as a data professional.
1. Orientation
This course dives deep into the data analytics context
2. Dataset and Tool Setup
Dataset overview and setup for Google Sheets, PostgreSQL, and Looker
3. Foundation
In-depth training on how to best give your data direction with data tools​
4. Measurement
A blueprint for extracting insights and moving from data to action
5. Immersion
Real-world case studies to help bring your data learnings to life
6. Sharing
Best practices for sharing analyses and your data work
7. Career Development
A collection of resources to help you kickstart a data career
8. Advanced/Specialty
An assortment of speciality data topics: GitHub, Looker, ...
 Datasets and Practice Q&A
Access custom built datasets, and 100+ practice question and answers

We build custom datasets to help set the foundation for a great learning environment. All datasets are available in Google Sheets, have downloadable SQL files to easily insert into your locally hosted database, and are uploaded to our Looker instance.

In addition, there's also over a hundred practice question and answer items to help you really flesh out your skills.
Accessing Data
What query would you write to access every entry in the Users dataset?

Sorting and Limiting Results
How would you write a query to pull only the Top 3 payment amounts from the Payments dataset?

Filtering Data
What query would you write to pull all payment data in 2018, or any payment that's been returned from the Payments dataset?

Segmentation
How would you write a query to create a new field that segments users into either a paid or organic registration?

Joining Multiple Datasets Together
What query would you write to join the Users and Payments datasets together, and identify the paid users that signed up on, or after June 1, 2018?
Accessing Data
What query would you write to access every entry in the Users dataset?
SELECT *
FROM getting_started.users
;

Sorting and Limiting Results

How would you write a query to pull only the Top 3 payment amounts from the Payments dataset?
SELECT payment_amount
FROM getting_started.payments
ORDER BY payment_amount
LIMIT 3 DESC
;

Filtering Data
What query would you write to pull all payment data in 2018, or any payment that's been returned from the Payments dataset?
SELECT *
FROM getting_started.payments
WHERE
 payment_timestamp BETWEEN '2018-01-01' AND '2018-12-31'
 OR
 payment_returned = TRUE
;

Segmentation
How would you write a query to create a new field that segments users into either a paid or organic registration?
SELECT
  *
, CASE WHEN acquisition_source = 'Social Media' THEN 'Paid'
  ELSE 'Organic' END AS acquisition_type
FROM getting_started.users
;

Joining Multiple Datasets Together
What query would you write to join the Users and Payments datasets together, and identify the paid users that signed up on, or after June 1, 2018?
SELECT *
FROM getting_started.users
INNER JOIN getting_started.payments ON users.user_id = payments.user_id
WHERE users.signup_timestamp >= '2018-06-01'
;

Read more about what we're thinking about.

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