Book Image

Cracking the Data Science Interview

By : Leondra R. Gonzalez, Aaren Stubberfield
Book Image

Cracking the Data Science Interview

By: Leondra R. Gonzalez, Aaren Stubberfield

Overview of this book

The data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company. Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you’ll find tips on job hunting, resume writing, and creating a top-notch portfolio. You’ll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview. By the end of this interview guide, you’ll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job.
Table of Contents (21 chapters)
Free Chapter
1
Part 1: Breaking into the Data Science Field
4
Part 2: Manipulating and Managing Data
10
Part 3: Exploring Artificial Intelligence
16
Part 4: Getting the Job

Calculating window functions

SQL window functions are an additional tool in your toolkit. Unlike aggregate functions, which return a single result per group of rows, window functions return a single result for each row, based on the context of that row within a window of related rows.

OVER, ORDER BY, PARTITION, and SET

Window functions have the following basic syntax:

<function> (<expression>)
OVER (
[PARTITION BY <expression_list>]
[ORDER BY <expression_list>] [ROWS|RANGE <frame specification>])

There are a few key concepts to understand here, so let’s break them down:

  • The OVER keyword is what differentiates a window function from a regular function; once you see it, you know you’re in window function land. The OVER clause defines the window or subset of rows within a query result set that the window function operates on. In short, it provides a way to partition the result set into logical groups and allows the window...