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Cracking the Data Science Interview

You're reading from  Cracking the Data Science Interview

Product type Book
Published in Feb 2024
Publisher Packt
ISBN-13 9781805120506
Pages 404 pages
Edition 1st Edition
Languages
Authors (2):
Leondra R. Gonzalez Leondra R. Gonzalez
Profile icon Leondra R. Gonzalez
Aaren Stubberfield Aaren Stubberfield
Profile icon Aaren Stubberfield
View More author details

Table of Contents (21) Chapters

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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: The split() method can be used to split s into individual words: words = s.split().

A block of code is set as follows:

x = 5
print(type(x)) # <class 'int'> 

Bold: Indicates a new term, an important word, or words that you see on screen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “The increased computing power and the development of advanced algorithms, especially in machine learning (ML) and deep learning (DL), have made it possible to efficiently process and analyze massive amounts of data.

Tips or important notes

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