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

## Clarification

Let’s start by clarifying the question. We have to specify many different factors with this, including the time frame. Was this a one-time event or a progressive movement?

A one-time event means that friend requests were at normal rates when they suddenly dropped by 10%, whereas a progressive event means that friend requests were at normal rates a month ago, but over this month, they declined progressively.

It could’ve been 9.8% yesterday, but today it’s 10%. By determining this time frame, we can begin to dive into the nuances and rule out certain causes. If it’s a one-time event, we can consider external factors versus internal factors.


# Clarification

Let’s start by clarifying the question. We have to specify many different factors with this, including the time frame. Was this a one-time event or a progressive movement?

A one-time event means that friend requests were at normal rates when they suddenly dropped by 10%, whereas a progressive event means that friend requests were at normal rates a month ago, but over this month, they declined progressively.

It could’ve been 9.8% yesterday, but today it’s 10%. By determining this time frame, we can begin to dive into the nuances and rule out certain causes. If it’s a one-time event, we can consider external factors versus internal factors.


Understand some specific clarifications we should ask for in our Facebook example. 

Introduction

Investigating Metrics

Fractional Metrics

Measuring Success

Feature Change

Metric Trade-Offs

Growth

Conclusion

Clarification and Gathering Context

Clarification