# Product Data Science

# Product Data Science

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

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

**You will learn real product data science!**

Course Sections

**12 sections with more than 100 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

Practical examples on to use regressions, ML, partial dependence plots, and rulefit to drive product development and come up with ideas for new product features

##### 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, i.e. fraud, ad clicks, conversation rate, email clicks, etc. These exercise 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. Include Uber and Airbnb case studies

##### 6) Fraud – Case Studies

Fraud is one of the most common data science application. 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 relevant 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. it focuses 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) 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

##### 10) Projects with solutions

Full product data science projects. Includes how to come up with ideas to improve conversion rate, how to predict fraud, and how to come up with ideas to increase retention

##### 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) Metrics via SQL

SQL exercises to create some of the most common metrics used by tech companies. I.e., identify power users or group by users based on their cross-device history. Queries rely heavily on window functions

Samples

#### 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

#### A/B tests: Data Products

How to test the first version of a data product isolating the effect of the algorithm vs the UI changes

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

Check out the full course curriculum

### 12 Sections, >100 lessons + exercises, real product data science problems using tech company tables, code in R and Python.

Course curriculum

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

Pricing

## $ 1625 2 monthly payments

☑ **Lifetime access to course curriculum **

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

☑ **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, skype chat, or share a Google doc with all the questions. You will get a prompt reply

☑ **Personalized feedback**

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

Enroll in course

### 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 >90 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

## Author

###### Giulio Palombo

Giulio Palombo worked as a data scientist for several top Silicon Valley tech companies, the last one being Airbnb.

He also wrote the books “A Collection of Data Science Take-Home Challenges” and “40 Data Science Product Questions” that have collectively sold >6K copies in ~3 years.

## FAQ

##### I am buying this with my employee training budget, do you provide a certificate or invoice that I can show to my employer?

Yeah, definitely. Can provide certificate, invoice, or really anything you need to show your employer to get reimbursed. Just ask for it.

##### Does this course also include the data science challenges and 40 case studies?

Yeah, 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, all challenges have a solution in the course, not just 4.

##### What’s the main difference between this course and the other million data science courses available?

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 long term effects, 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.