Learn the fundamentals and strategies for interviewing for 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 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

## Measuring success
Measuring the success of products is critical to data science and analytics interviews. Generally, this type of interview question is an encapsulation of every time a product manager or executive asks the question: “So, how is it doing?”

The answer to this question should be quick and easy to understand at a high level. As a data scientist, the interviewer is always interested in understanding how we tackle a full-fledged analysis in a way that communicates all the information efficiently.

Examples of questions in this metric usually ask about a particular feature within a product, such as:

* How do we measure success for Facebook Events?
* How would we measure success for Yelp reviews?
* How would we measure the success of acquiring new users on Netflix through a free trial?
* How would we measure the success of private stories on Instagram, where only certain chosen friends can see the story?
* What metrics would we track for analyzing the health of Google Docs?

## Why measuring success questions are asked
So, what is the interviewer looking for when asking these questions?

**Understanding the scope**

Can we ask clarifying questions to understand more about what we’re dealing with?

For example, what does the product do, and who is the target audience? What is the goal of the feature or product we’re working on? Why is it important to the business?

**The structure of the answer**

Are we randomly brainstorming ideas  or do we structure points with examples and metrics? How would we implement strategies or generalize these ideas without metrics?

**Business intuition**

Lastly, make sure to understand how to answer the problem intelligently by using metrics to support answers. Are we creating hypotheses that make sense and could be backed up by these metrics?

# Measuring success
Measuring the success of products is critical to data science and analytics interviews. Generally, this type of interview question is an encapsulation of every time a product manager or executive asks the question: “So, how is it doing?”

The answer to this question should be quick and easy to understand at a high level. As a data scientist, the interviewer is always interested in understanding how we tackle a full-fledged analysis in a way that communicates all the information efficiently.

Examples of questions in this metric usually ask about a particular feature within a product, such as:

* How do we measure success for Facebook Events?
* How would we measure success for Yelp reviews?
* How would we measure the success of acquiring new users on Netflix through a free trial?
* How would we measure the success of private stories on Instagram, where only certain chosen friends can see the story?
* What metrics would we track for analyzing the health of Google Docs?

# Why measuring success questions are asked
So, what is the interviewer looking for when asking these questions?

**Understanding the scope**

Can we ask clarifying questions to understand more about what we’re dealing with?

For example, what does the product do, and who is the target audience? What is the goal of the feature or product we’re working on? Why is it important to the business?

**The structure of the answer**

Are we randomly brainstorming ideas  or do we structure points with examples and metrics? How would we implement strategies or generalize these ideas without metrics?

**Business intuition**

Lastly, make sure to understand how to answer the problem intelligently by using metrics to support answers. Are we creating hypotheses that make sense and could be backed up by these metrics?

Learn the basics of measuring success questions and why they're asked in interviews. 


Introduction to Measuring Success Questions

Learn the basics of measuring success questions and why they're asked in interviews.

Introduction

Investigating Metrics

Fractional Metrics

Measuring Success

Feature Change

Metric Trade-Offs

Growth

Conclusion

Introduction to Measuring Success Questions

Measuring success

Why measuring success questions are asked