Product Data Science Course

Product Data Science Course



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




 

From the same author of A Collection of Data Science Take-Home Challenges that sold >6K copies in ~2 1/2 years

 

 

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

Lesson samples


Logistic Regression

When to choose a logistic regression, how to interpret it, and how to use its output to come up with new test 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 all 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

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

 

Everything you can possibly need to know to work as a data scientist in product or analytics

 

 

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

Sharing the course with your team?


Several people at your organization would benefit from taking this course?
Email info@datamasked.com for group licences or on-site training

Author


Giulio Palombo
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 ~2 1/2 years.

On Data Masked homepage you can find all info about the books, testimonials, and more.

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.

To buy the course with my training budget, I need to show that the course content is relevant to my job duties. Can you help?

Yes, absolutely. Just email info@datamasked.com with a brief overview of the most important data science projects you currently have at work.
If there is a match between the course content and your job duties, I will get back to you with clear examples of how your employer would benefit from you taking the course.

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

Yeah, all those challenges and product questions are here too. However, this course includes solutions for all the challenges, not just 4.

Beside that, 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.

I couldn’t find an answer to my question here!

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