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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
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Aaren Stubberfield
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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

Visualizing Data and Data Storytelling

Data visualization is the process of creating images, charts, and other visual data. This is performed to reveal and understand underlying trends and patterns in the data. These skills are important in order for data scientists to tell compelling data stories. For example, a marketing analyst may examine online customer behavior to identify purchasing habit trends such as seasonal trends, product preferences, or demographic correlations. These patterns can be used to craft targeted marketing campaigns or develop personalized recommendations, enhancing customers. Alternatively, an analyst may analyze historical financial time series data to identify patterns in market trends, stock performance, or economic indicators. By recognizing patterns, they can make informed predictions about future market behavior, guide investment decisions, and develop risk management strategies.

In this chapter, you will delve into the world of data visualization...

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