Murach's Python for Data Science (2nd Edition): Training and Reference
These days, businesses are collecting massive amounts of data. But this data isn’t valuable until someone analyzes it to gain insights that can be used to make decisions. That’s why the US Bureau of Labor Statistics (BLS) predicts that the demand for data analysts will continue to grow for the rest of the decade.

Now, with Murach’s Python for Data Science as your guide, you’ll learn how to use Python libraries to get, clean, prepare, and analyze data at a professional level. To start, you’ll learn how to use Pandas for data analysis and Seaborn for data visualization. Then, you’ll learn how to use Scikit-learn to create regression models that you can use to make predictions.

To tie everything together, this book contains four realistic analyses that use real-world data. That’s because studying analyses like these is critical to the learning process.

1145212960
Murach's Python for Data Science (2nd Edition): Training and Reference
These days, businesses are collecting massive amounts of data. But this data isn’t valuable until someone analyzes it to gain insights that can be used to make decisions. That’s why the US Bureau of Labor Statistics (BLS) predicts that the demand for data analysts will continue to grow for the rest of the decade.

Now, with Murach’s Python for Data Science as your guide, you’ll learn how to use Python libraries to get, clean, prepare, and analyze data at a professional level. To start, you’ll learn how to use Pandas for data analysis and Seaborn for data visualization. Then, you’ll learn how to use Scikit-learn to create regression models that you can use to make predictions.

To tie everything together, this book contains four realistic analyses that use real-world data. That’s because studying analyses like these is critical to the learning process.

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Murach's Python for Data Science (2nd Edition): Training and Reference

Murach's Python for Data Science (2nd Edition): Training and Reference

by Scott McCoy
Murach's Python for Data Science (2nd Edition): Training and Reference

Murach's Python for Data Science (2nd Edition): Training and Reference

by Scott McCoy

Overview

These days, businesses are collecting massive amounts of data. But this data isn’t valuable until someone analyzes it to gain insights that can be used to make decisions. That’s why the US Bureau of Labor Statistics (BLS) predicts that the demand for data analysts will continue to grow for the rest of the decade.

Now, with Murach’s Python for Data Science as your guide, you’ll learn how to use Python libraries to get, clean, prepare, and analyze data at a professional level. To start, you’ll learn how to use Pandas for data analysis and Seaborn for data visualization. Then, you’ll learn how to use Scikit-learn to create regression models that you can use to make predictions.

To tie everything together, this book contains four realistic analyses that use real-world data. That’s because studying analyses like these is critical to the learning process.

Product Details

ISBN-13: 9781943873173
Publisher: Mike Murach and Associates, Inc.
Publication date: 05/15/2024
Edition description: 2nd ed.
Pages: 592
Product dimensions: 7.90(w) x 9.90(h) x 1.30(d)

About the Author

Scott McCoy is a professional programmer, and has worked in a variety of domains including bioinformatics, IT automation, security, and data analytics. He holds a B.S. in computer science and a Microsoft certification in database technologies. He is a full-time author for Murach Books. In his free time, Scott enjoys reading, hiking, and spending time with his family.

Table of Contents

Section 1 Get off to a fast start

Chapter 1 Introduction to Python for data analysis

Chapter 2 The Pandas essentials for data analysis

Chapter 3 The Pandas essentials for data visualization

Chapter 4 The Seaborn essentials for data visualization

Section 2 The critical skills for success on the job

Chapter 5 How to get the data

Chapter 6 How to clean the data

Chapter 7 How to prepare the data

Chapter 8 How to analyze the data

Chapter 9 How to analyze time-series data

Section 3 An introduction to predictive analysis

Chapter 10 How to make predictions with a linear regression model

Chapter 11 How to make predictions with a multiple regression model

Section 4 The case studies

Chapter 12 The Polling case study

Chapter 13 The Forest Fires case study

Chapter 14 The Social Survery case study

Chapter 15 The Sports Analytics case study

Reference aids

Appendix A How to set up Windows for this book

Appendix B How to set up macOS for this book

Index

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