Product Data Science

Learn how to develop product sense, create metrics, and design robust A/B tests

Some companies that approved this course as a professional development expense

You will learn

Data-driven hypotheses for new A/B tests

Learn what was the data-driven hypothesis behind FB stories, Netflix free trial, Youtube Premium, etc.

Design actionable metrics

When to use average-based vs threshold-based metrics? How to identify those thresholds in metrics such as percentage of users with 7 friends in 10 days?

Evaluate metric trade-offs

How to evaluate changes that positively affect some key metrics, but negatively affect others? Which strategy to use to decide whether a product change is good or not in those cases?

A/B Testing

How to design statistically sound A/B tests? How to choose test traffic percentage? How to deal with novelty effect, connected users, randomization bugs, or other typical issues in tech A/B tests?

Applied data

How to deal with actual tech data, such as sudden spikes or drops, bugs, unbalanced metrics, non-random missing data, A/B test results, conversion data, etc.

Machine Learning

How to use machine learning to fight frauds, build data products, or develop product insights? How to evaluate the impact of new machine learning models into production?

“ One of the best decisions I’ve made for my career. The case studies contain information that I wasn’t able to find anywhere else because most of them are gained with years of experience. ”

Bahar Bazargan – got a job as a Data Scientist @ Facebook – US


Product Case Study – Insights

Should a free trial require credit card info? Where in the funnel should the highest friction point be?

See the lesson ->

Logistic Regression

When to choose a logistic regression, how to interpret it, and how to use its output to come up with product ideas

See the lesson ->

A/B tests: Sample Size

How to estimate for how many days you should run an A/B test, from both a statistical and business perspective

See the lesson ->

A/B tests: Novelty Effect

How to understand if a test is affected by novelty effect and how to deal with it in practice

See the lesson ->

A/B Tests: Randomization

The crucial step of making sure that test and control are properly randomized

See the lesson ->

Metrics: Ads Challenge Solution

How to pick a metric for ads, identify the best performing ones, and analyze trends

See the lesson ->

“ I have taken several DS online courses, and this is the best one. It is extremely realistic.

The skills I learned made me a better data scientist and helped me tremendously in my daily job.

The 1:1 mentorship part is great too. I would always get a reply almost right away and complex concepts were explained very clearly ”

Kenny Tang – Data Scientist @ SmarTone Telecom Lim – Hong Kong

You will learn real product data science

Course sections

12 sections with more than 130 lessons and exercises in total
Real tech company tables, i.e. user table, event table, A/B test tables, etc.

1) Product Sense via Machine Learning

Learn what was the data-driven hypothesis behind FB stories, Netflix free trial, Youtube Premium, etc.

2) Product and Metrics – Case Studies

18 case studies on how to design actionable metrics, understand what drives them, and figure out how to improve them via new product features

3) Personalization

In depth practical exercise on how to use machine learning to build a data product personalized at the user level. This is the framework used to optimize almost all data products

4) Unbalanced Classes

Almost all tech company data have unbalanced classes, e.g. fraud, ad clicks, conversation rate, email clicks, etc. These exercises explain how to deal with that

5) Missing data in tech

Most of missing data in tech are non-random, i.e. users choose to not provide certain information about themselves. These lessons explain how to deal with biased missing data, including Uber and Airbnb case studies

6) Fraud – Case Studies

Fraud is one of the most common data science applications. These case studies explain how to set up the problem from a ML standpoint as well the how to build a product around it

7) A/B Testing – Practice

A series of lessons covering all that’s needed to know about A/B testing. Includes statistical inference theory as well as very practical tech problems (novelty effect, randomization, sample size, testing by market, etc.)

8) A/B testing – Case Studies

12 case studies describing how top tech companies design their A/B tests. They focus on the most common issues tech companies face, like how to test in social networks or marketplaces, how to estimate long term effects, when A/B tests fail, etc.

9) New Product Case Studies

A new list of product case studies. They focus on trade-offs when evaluating multiple metrics at the same time (e.g. what to do if after a test some key metrics are up and some down) as well as explaining the thought process behind using data to choose whether testing a totally new product

10) Collection of tech company blog posts/case studies

This is a collection of company write-ups, tutorials, and blog posts. Includes Airbnb, FB, Linkedin, Google, Netflix and many more other companies describing how they design A/B tests and use DS to drive product development

11) Data challenges with solutions

They come from the “Collection of data science takehome challenges” book. They touch all the topics taught in the course. All challenges come with full solution in R and Python

12) Coding course

Teaches a reusable framework to solve coding exercises in SQL, R, and Python. Includes exercises about metrics, data processing, aggregate statistics as well as probability exercises

Can I pay for this via my employee training budget?

Yes, the course perfectly fits most employee training requirements. Approval process at most companies has been straightforward.

This course has been approved by managers at >200 companies of different sizes (from FAANG to 20-people start-ups), industries (tech, banks, consulting, healthcare), and locations (North America, Asia, Europe).

If you need any help to facilitate the reimbursement process, need help in matching course content to your current work tasks, or want to see a sample of the invoice you could receive after getting the course, please get in touch.

  •   Lifetime access to course curriculum and all its updates

    Curriculum includes a mix of theoretical lessons, product case studies, and challenges with solution.

    In the last couple of years, updates included tons of brand new and constantly up-to-date product case studies as well as an entire course on coding for data science

  •   Unlimited 1:1 support from course author for 1 year

    Any questions you have about the course material or anything related to product data science, you can send an email, chat, or share a Google doc with all the questions. You will get a prompt reply

  •   Data science coding course

    When you enroll in the product data science course, you will also automatically get enrolled in the data science coding course from the same author. The coding course teaches a framework to tackle common data science coding exercises in SQL, Python, and R

  •   Personalized feedback

    Send your solution for all the exercises in the course. You’ll get a detailed feedback on your work

“ Lessons, exercises, and projects are great to improve data science skills and product sense. I learned a lot by going through them ”

Doudou Tang – Data Scientist @ – UK

I have been creating educational material related to data science since 2015. Firstly focusing on job interviews (A Collection of Data Science Take-Home Challenges, 40 Data Science Product Questions, DS Coding Course) and, more recently, focusing on employee training (Full Course in Product Data Science and Chatgpt for Data Scientists).

The Full Course in Product Data Science was among the fastest courses ever to reach $1 million in sales on Teachable. The main idea behind it is to simulate the actual work data scientists do in top tech companies.

In 2023, I built and sold a Chatgpt app to Colgate. Based on that experience, I created a Chatgpt for Data Scientists course to help data scientists build and monetize AI apps, and built the DataScienceGPT app.

Prior to all this, I worked as a data scientist for several Silicon Valley tech companies, the last one being Airbnb.

Yes, definitely. Can provide certificate, invoice, or anything else you need.

Yes, all those challenges and product questions are here too. However, this course includes much more.

Its main focus is on teaching product data science via a combination of theoretical lessons and practical examples. The challenges then come at the end to make sure things were learned properly.

Also, the course includes many new product questions, like for instance the first one in the samples above, as well as all the challenges have a solution, not just 4. Here is the full curriculum.

Frankly speaking, most data science courses are not particularly useful because they have little/nothing to do with what data scientists actually do at work.

Firstly, data scientists don’t spend their time over-tuning a fancy model to marginally improve its performance. In fact, most of data science work is about looking at the data to come up with product ideas and properly designing A/B tests.

And the few applied courses tend to be so simple to the point of being highly unrealistic. I.e., most A/B test courses will teach you to randomly split users, run the two versions of the site, and check the results. But that strategy almost never works for several reasons (test and control would never be independent in social networks or marketplaces, can’t estimate novelty effect, etc.).

This course will teach you how product data science is actually done at top tech companies. The course uses data that look exactly like tech company tables (including wrong entries, non-random missing values, A/B tests, etc.). And then actual data science projects are built on top of those data.

For any additional questions, please email

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