Learn how to prepare for the wide range of questions that come up in data science interviews.

Data Science Interview

What is Data Science?

How Is Data Science Tested in Interviews?

Types of Data Science Interview Questions

Data Science Coding Skills

Data Science Technical Skills

Data Science Business Applications

Preparing for Data Science Interviews

Overview of the Data Science Learning Path

Introduction to Data Science

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

Let's tackle advanced SQL interview questions that focus on multi-joins and layers of data interpretation. These questions may come up in take-home challenges and senior level interviews.

Introduction to hard SQL questions

Swipe Precision

Cumulative Distribution

Rolling Bank Transactions

Subscription Overlap

First Touch Attribution

Search CTR

Paired Products

Payments Received

Hard SQL Questions

Data structures in Python attempt to be more intuitive and flexible than traditional data structures in other programming languages.

Introduction

Basic Data Types

Iterables

Functions and Classes

Find the Missing Number

Find Bigrams

Merge Sorted Lists

Permutation Palindrome

Equivalent Index

Data Structures

As we said in the first section of this course, a major benefit of using Python for data science in comparison to other programming languages is the availability of a large number of useful packages that are distributed under a free license. 

numpy

scipy

matplotlib

pandas

sklearn

Over 100 Dollars

Good Grades and Favorite Colors

Generate Normal Distribution

Random Seed Function

Complete Addresses

Common DS Packages

Let's try some hard Python questions that you would see in tougher data science interviews and many machine learning interviews.

Max Width

Scrambled Tickets

Bernoulli Sample

Sum to N

Check Normality

Pool Matching

Python Questions: Hard

Probability Theory is the branch of mathematics that deals with uncertainty, underpinning all of statistics and machine learning.

Probability Axioms

Implications of the Probability Axioms

Conditional Probability

Bayes' Rule

Combinatorics

Lazy Raters

Fake Algorithm Reviews

Three Zebras

Poker Pair

Basic Probability

All areas of study in math can roughly be divided into two camps: discrete mathematics and continuous mathematics. Perhaps the best way to describe the difference between the two is to talk about what each of the branches means by "number."

Discrete Random Variables

Expected Values

Variance

Discrete Uniform Distributions

Bernoulli and Binomial Distributions

Geometric Distributions

Poisson Distributions

N Dice

Fair Coin

Converted Sessions

Biased Five Out of Six

Discrete Distributions

Continuous probability distribution: A probability distribution in which the random variable X can take on any value (is continuous).

Continuous Random Variables

The Normal Distribution, From Scratch

2X - Y

Non-normal Probability Distribution

Swimmer Survival

Continuous Distributions

Thus far in this course, we have considered random variables under an idealized scenario where we know the distribution of the random variable.

Sample vs. Population

Convergence Theorems of Samples

500 Cards

First to Six

Unbiased Estimator

Ad Raters: Part 2

Sampling Theorems

Hypothesis testing covers the fundamental theory and background behind A/B Testing. In this course we'll cover Z and T test, multiple hypothesis testing, and the different type errors.

Fundamentals of Hypothesis Testing

The Z and t test

Z and t Tests

Two-sample Tests

Proportion Testing

Different Parcel Effectiveness

Analysis of Variance (ANOVA)

Hundreds of Hypotheses

Non-parametric Tests

Non-Normal AB Testing

Hypothesis Testing

Confidence intervals help us deal with this imprecision by giving us a way to talk about a range of values with some certainty where the true value of the statistic is contained in.

Introduction to Confidence Intervals

Confidence Interval Fundamentals

Confidence Intervals for Statistics

Confidence Interval Explanation

Bounds for Coffee and Tea Drinkers

Reducing Error Margin

Confidence Intervals

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

The next couple of chapters will cover common scenarios and concepts involved in A/B testing. As A/B testing involves statistical concepts, there may be terms that you need refreshing on.

Overview

Multiple Comparisons

Debugging A/B Tests

Holdback Effects

Cannibalization

Non-Normal A/B Testing

Random Bucketing

Sample Size Bias

Twenty Variants

A/B Testing Common Scenarios

There are scenarios where A/B testing is not necessarily the best course of action. Often, there are technical, infrastructure, or practical concerns that come up while planning an A/B test.

When you shouldn't A/B test

Before and After Testing

Island Testing

Common Pitfalls

Network Experiment Design

A/B Test Ties

A/B Testing Tradeoffs

This is a refresher on some important statistical concepts that will help us with A/B testing and beyond. While by no means a comprehensive guide, this chapter will go over some important basics about statistical testing and probability distributions.

Statistical Concepts

Probability Distributions

Advanced Concepts

Estimating D

Location Feature Sharing

Significance Time Series

Time on FB Distribution

Survey Response Randomness

Incorrect Packets

Statistics

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

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

Diagnosing and Investigating Metrics

Data science helps businesses make smarter decisions by combining programming, statistics, modeling, and machine learning skills. Learn how to ace data science job interviews with a well-structured approach.

Master the wide range of data science interview questions.

Data Science Interview

Significance Time Series
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Data Science Interview

Significance Time SeriesGo to question details page

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Significance Time Series
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