Data Analytics

Let's learn how to approach data analytics interviews and understand the different types of analytics interview questions that will show up for data analyst and data science roles.

In this course we’ll go over the core concepts of causality, significance, and analyzing data. This is meant as a quick refresher and a high level overview of causal inference basics to eventually apply them in data analytics problems.

Investigating metrics is a type of product intuition problem that will come up frequently in interviews. Examples of this are typically phrased along the lines of - If X metric is up/down by Y percent, how would you investigate it?

Get started on tackling easy level SQL questions involving aggregations, joining multiple tables, and pulling data for beginning analytical reports.

Medium level SQL questions utilize more advanced concepts like sub-queries, window functions, and solving case study problems.

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

Let's start with a general framework for A/B testing. In practice, an A/B testing and experimentation all follow a step by step process of setting metrics and designing experiments.

Business health metrics tackle case studies and questions focused on setting and defining core metrics for different business priorities.