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

## Evaluation 

This section of the framework is especially important, given that there are a lot of similarities between these kinds of questions and the actual job of product managers and business analysts. In this last part of the lesson, we have to think about the problem from a data perspective by testing hypotheses and picking metrics to validate them.

The best way to pick metrics to validate is by visualizing our analysis as a graph or a two-dimensional representation of multiple metrics over time for investigative metrics.

We can plot a bar graph or line graph with an axis pair, such as friend requests over time. We can also plot a line graph with more than one axis pair, such as friend requests over time and accepted requests over time.


# Evaluation 

This section of the framework is especially important, given that there are a lot of similarities between these kinds of questions and the actual job of product managers and business analysts. In this last part of the lesson, we have to think about the problem from a data perspective by testing hypotheses and picking metrics to validate them.

The best way to pick metrics to validate is by visualizing our analysis as a graph or a two-dimensional representation of multiple metrics over time for investigative metrics.

We can plot a bar graph or line graph with an axis pair, such as friend requests over time. We can also plot a line graph with more than one axis pair, such as friend requests over time and accepted requests over time.


Visualize the data to think of ways to validate your hypotheses.

Introduction

Investigating Metrics

Fractional Metrics

Measuring Success

Feature Change

Metric Trade-Offs

Growth

Conclusion

Evaluation and Validation

Evaluation