
Foundational Python for Data Science, 1st edition
Published by Pearson (October 7, 2021) © 2022
Kennedy Behrman
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Title overview
Data science and machine learning—two of the world's hottest fields—are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world's #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help students with widely diverse backgrounds learn foundational Python so they can use it for data science and machine learning. This book is catered to introductory-level college courses on data science.
Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once students have learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more—all created with Colab (Jupyter compatible) notebooks, so students can execute all coding examples interactively without installing or configuring any software.
- Master Google Colab notebook Data Science programming
- Manipulate data with popular Python libraries such as: pandas and numpy
- Apply Python Data Science recipes to real world projects
- Learn functional programming essentials unique to Data Science
- Access case studies, chapter exercises, learning assessments, comprehensive Jupyter based Notebooks, and a complete final project
Table of contents
Preface xiiiI: Learning Python in a Notebook Environment 1
1 Introduction to Notebooks 3II: Data Science Libraries 83
2 Fundamentals of Python 13
3 Sequences 25
4 Other Data Structures 37
5 Execution Control 55
6 Functions 67
7 NumPy 85III: Intermediate Python 171
8 SciPy 103
9 Pandas 113
10 Visualization Libraries 135
11 Machine Learning Libraries 153
12 Natural Language Toolkit 159
13 Functional Programming 173
14 Object-Oriented Programming 187
15 Other Topics 201
A Answers to End-of-Chapter Questions 215
Index 221
Author bios
Kennedy Behrman is a veteran software and data engineer. He first used Python writing asset management systems in the Visual Effects industry. He then moved into the startup world, using Python at startups using machine learning to characterize videos and predict the social media power of athletes.
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