The ultimate guide to product analytics interviews. Strategies developed by FAANG engineers to craft structured, impactful answers and increase your chances of landing a data science job.

Product analytics interviews evaluate your skills and creativity as an aspiring data analyst. Interview questions evaluate if you can use your technical knowledge to guide the further development of a product. Learning how to answer these questions is crucial to ace most job interviews for data scientists and analysts.

In this course, you’ll first gain an overview of what product analytics interviews look like and what kinds of questions usually come up. Next, you’ll learn to use a framework for answering product analytics questions, showcasing your analytical skills. Finally, you’ll learn how to apply said framework to different kinds of product analytics interview questions by looking at several examples.

With this course, you’ll increase your chances of landing a job in data science by learning how to effectively answer product analytics interview questions. You’ll be able to provide well-structured and well-communicated answers, and show you can creatively tackle any business problem you may face.

Ace the Product Analytics Job Interview

# Problem

[Let's say we're working on Facebook Groups. A product manager decides to add threading to comments on group posts. We see comments per user increase by 10%, but posts go down 2%. Why would that be?](https://www.interviewquery.com/questions/threaded-comments)

Additionally, what metrics would prove our hypotheses?

Try answering this on your own before continuing. Use the hints below if you are stuck.




What is threading, and how does it change the relationship between comments and posts?


Threading restructures the flow of comments so that instead of responding to the post, users can now respond to individual comments beneath the post. What effect might this have on a push notification ecosystem?


Original posters may receive fewer push notifications since notifications are now being sent on the basis of individual comments and not the post as a whole. What metrics might we want to look at to determine the effect of threading on the post/comment infrastructure?

# Solution

When comments are added to threads, we can theorize that the following changes happen within Group postings:

* Ideas become bucketed and structured, making it easier to search, navigate, and respond to different users with threads.
* Responders are forced to stay on the thread instead of posting a new top-level comment or post. This can create new dynamics within user workflows and notifications, with only users within the specific thread being notified as opposed to the entire comment group and the original poster.
* We can theorize that threading pushes fewer notifications to the poster (and more to the top-level commenter). This would mean fewer push notifications that bring people back to their devices and, therefore, less posting in general. More discussion would happen within the threads, which could cause the notification system to increase comments. 
* The order in which your comment appeared within the post becomes irrelevant, so there's no barrier to posting over shortened periods of time. This allows deeper discussions as the time barrier disappears.
* Late readers may find their answers already if the same question has been previously asked, preventing duplicate posts.
* Responding to specific comments becomes possible as opposed to regular comments, which are first in, first out.



Try to solve this question to test your understanding of the material.


Sample Question: Threaded Comments

Try to solve this question to test your understanding of the material.

Introduction

Investigating Metrics

Fractional Metrics

Measuring Success

Feature Change

Metric Trade-Offs

Growth

Conclusion

Sample Question: Threaded Comments

Problem