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Cracking the Data Science Interview

You're reading from   Cracking the Data Science Interview Unlock insider tips from industry experts to master the data science field

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781805120506
Length 404 pages
Edition 1st Edition
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Authors (2):
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Leondra R. Gonzalez Leondra R. Gonzalez
Author Profile Icon Leondra R. Gonzalez
Leondra R. Gonzalez
Aaren Stubberfield Aaren Stubberfield
Author Profile Icon Aaren Stubberfield
Aaren Stubberfield
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Toc

Table of Contents (21) Chapters Close

Preface 1. Part 1: Breaking into the Data Science Field FREE CHAPTER
2. Chapter 1: Exploring Today’s Modern Data Science Landscape 3. Chapter 2: Finding a Job in Data Science 4. Part 2: Manipulating and Managing Data
5. Chapter 3: Programming with Python 6. Chapter 4: Visualizing Data and Data Storytelling 7. Chapter 5: Querying Databases with SQL 8. Chapter 6: Scripting with Shell and Bash Commands in Linux 9. Chapter 7: Using Git for Version Control 10. Part 3: Exploring Artificial Intelligence
11. Chapter 8: Mining Data with Probability and Statistics 12. Chapter 9: Understanding Feature Engineering and Preparing Data for Modeling 13. Chapter 10: Mastering Machine Learning Concepts 14. Chapter 11: Building Networks with Deep Learning 15. Chapter 12: Implementing Machine Learning Solutions with MLOps 16. Part 4: Getting the Job
17. Chapter 13: Mastering the Interview Rounds 18. Chapter 14: Negotiating Compensation 19. Index 20. Other Books You May Enjoy

What this book covers

In Chapter 1, Exploring the Modern Data Science Landscape, we begin our journey with a brief but valuable overview of the contemporary landscape of data science and AI.

In Chapter 2, Finding a Job in Data Science, we will introduce data science roles and their various categories.

In Chapter 3, Programming with Python, you will familiarize yourself with the most common and useful tasks and operations in the Python language.

In Chapter 4, Visualizing Data and Storytelling, you will learn techniques for telling engaging data stories.

In Chapter 5, Querying Databases with SQL, you will dive into the world of databases, understanding their design and how to query them to acquire data.

In Chapter 6, Scripting with Bash and Shell Commands in Linux, you will boost your operating system skills with the power of bash and shell commands, enabling you to interface with multiple technologies either locally or in the cloud.

In Chapter 7, Using Git for Version Control, we explore the most useful commands in Git for project collaboration and reproducibility.

In Chapter 8, Mining Data with Probability and Statistics, you will understand some of the most relevant topics in probability and statistics that serve as the foundation for many ML models and assumptions.

In Chapter 9, Understanding Feature Engineering and Preparing Data for Modeling, you will use your understanding of descriptive statistics to create clean, “machine-legible” datasets.

In Chapter 10, Mastering Machine Learning Concepts, you will learn about the most used ML algorithms, their assumptions, how they work, and how to best evaluate their performance.

In Chapter 11, Building Networks with Deep Learning, we take a step further into building and evaluating neural networks in various applications while also touching base on the latest advancements in AI.

In Chapter 12, Implementing Machine Learning Solutions with MLOps, we will review the data science process, tools, and strategies to effectively design and implement an end-to-end ML solution.

In Chapter 13, Mastering the Interview Rounds, you will learn the best techniques to successfully bypass technical and non-technical factors at every stage of the interview process.

In Chapter 14, Negotiating Compensation, you will learn to optimize your earning potential.

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