Cracking the Data Science Interview: Unlock insider tips from industry experts to master the data science field

Key Features

• Acquire highly sought-after skills of the trade, including Python, SQL, statistics, and machine learning
• Gain the confidence to explain complex statistical, machine learning, and deep learning theory
• Extend your expertise beyond model development with version control, shell scripting, and model deployment fundamentals
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

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.

What you will learn

• Explore data science trends, job demands, and potential career paths
• Secure interviews with industry-standard resume and portfolio tips
• Practice data manipulation with Python and SQL
• Learn about supervised and unsupervised machine learning models
• Master deep learning components such as backpropagation and activation functions
• Enhance your productivity by implementing code versioning through Git
• Streamline workflows using shell scripting for increased efficiency

Who this book is for

Whether you're a seasoned professional who needs to brush up on technical skills or a beginner looking to enter the dynamic data science industry, this book is for you. To get the most out of this book, basic knowledge of Python, SQL, and statistics is necessary. However, anyone familiar with other analytical languages, such as R, will also find value in this resource as it helps you revisit critical data science concepts like SQL, Git, statistics, and deep learning, guiding you to crack through data science interviews.

Table of Contents

1. Exploring the Modern Data Science Landscape
2. Finding a Job in Data Science
3. Programming with Python
4. Visualizing Data and Data Storytelling
5. Querying Databases with SQL
6. Scripting with Shell and Bash Commands in Linux
7. Using Git for Version Control
8. Mining Data with Probability and Statistics
9. Understanding Feature Engineering and Preparing Data for Modeling
10. Mastering Machine Learning Concepts
11. Building Networks with Deep Learning
12. Implementing Machine Learning Solutions with MLOps
13. Mastering the Interview Rounds
14. Negotiating Compensation

Leondra R. Gonzalez is a data scientist at Microsoft and Chief Data Officer for tech startup CulTRUE, with 10 years of experience in tech, entertainment, and advertising. During her academic career, she has completed educational opportunities with Google, Amazon, NBC, and AT&T.

Aaren Stubberfield is a senior data scientist for Microsoft's digital advertising business and the author of three popular courses on Datacamp. He graduated with an MS in Predictive Analytics and has over 10 years of experience in various data science and analytical roles focused on finding insights for business-related questions.