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. 

Data Analytics

Overview of Data Analytics

How Does Data Analytics Get Tested in the Interview?

Types of Data Analytics Interview Questions

Introduction to Data Analytics

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.

What is Causal Inference?

Determining Causality

Causal Inference Case Study

Advanced Causal Inference Techniques

Causal Inference Tradeoffs

Boarding Times Bias

Email Discount Effectiveness 

Approval Drop

Location Feature Sharing

Data Analytics Fundamentals: Causal Inference

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?

Introduction to Investigating Metrics Questions

Recommended Framework

Clarifying Questions

Gathering Context

Hypothesizing (Segmentation)

Evaluation and Validation

Example Case Study and Solution

Decreasing Comments

Investigating Fractional Metrics

Declining Applicants

Post Composer Drop

Fill Rate Drop

Diagnosing and Investigating Metrics

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

Introduction to Easy SQL questions

SQL JOINs

Download Facts

GROUP BY and HAVING

Total Spent On Products

Largest Salary By Department

ORDER BY and LIMIT

Average Quantity

Miscellaneous Functions and Clauses

Manager Team Sizes

Session Difference

Top 5 Turnover Risk

Easy SQL Questions

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

Introduction to medium SQL questions

DATETIME

Self-JOINs and CROSS JOINs

Subqueries and CTEs

Daily Logins

Employee Salaries (ETL Error)

Slacking Employees Salaries

CASE clause

Exam Scores

Customer Orders

Employee Salaries (Top 3 departments)

Handling NULL values

Empty Neighborhoods

Distance Traveled

LIKE and other useful functions

Window Functions

Last Transaction

Top Three Salaries

User Experience Percentage

Medium SQL Questions

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?”.

Overview

Strategy and Framework

Clarification

Understand the Product and Set Goals

Define Success Metrics

Prioritize Metrics and Test Hypothesis

Summary And Tips

Fractional Shares

Group Success

Instagram TV

Close Friends Story

Measuring Success

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.

General Framework

Before Testing

Setting Metrics

Experiment Design

Randomization

Analyze Results

Button AB Test

Experiment Validity

P-value to a Layman

New UI Effect

A/B Testing & Experiment Design

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

Introduction to Business Health Metrics

What is Business Health?

Determine Business Priorities

Identifying Input and Output Metrics

Ranking and Prioritization

Demand Metrics

D2C Socks e-Commerce

Community Health Metrics

Business Health Metrics

Data Analytics is the applied use of data to drive desision making. This learning path will help you understand and analyze data to  develop insights, make better decisions and ultimately improve the company you work for!

In this course we will learn how to tackle data analytics problems

 <h3>Statistical Concepts Review</h3>

<p>Let’s start by reviewing a few key concepts for measuring and constructing our experiment.</p>

<p><strong>Significance</strong> is defined as the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference. [1]</p>

<p><strong>P-values</strong> are the probability of obtaining an

Data Analytics

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