Python for Data Science For Dummies

The fast and easy way to learn Python programming and statistics

Python is a general-purpose programming language created in the late 1980s--and named after Monty Python--that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library.

Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud.

• Get started with data science and Python

• Visualize information

• Wrangle data

• Learn from data

The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.

Autorentext

John Paul Mueller is a tech editor and the author of over 100 books on topics from networking and home security to database management and heads-down programming. Follow John's blog at http://blog.johnmuellerbooks.com/. Luca Massaron is a data scientist who specializes in organizing and interpreting big data and transforming it into smart data. He is a Google Developer Expert (GDE) in machine learning.

Klappentext

• Learn Python data analysis programming and statistics
• Write code in the cloud with Google Colab™

• Wrangle data and visualize information Relax! Data science doesn't have to be scary Curious about data science, but a bit intimidated? Don't be! This book shows you how to use Python to do all sorts of cool things with data science. You'll see how to install the Anaconda tool suite, so working with Python is a breeze. You'll discover Google Colab, which lets you write code in the cloud using your tablet. You'll find out how to perform all kinds of interesting calculations using the latest version of Python. And you'll learn to use the various libraries that enable scientific statistical analysis, plotting and graphing, and much more. Inside...

• Python set-up for data science
• Working with Jupyter Notebook
• Conditioning and shaping data
• Graphing with MatPlotLib
• Ways to analyze your data
• Getting more from Python
• Useful data science algorithms
• Ten essential data resources

Zusammenfassung
The fast and easy way to learn Python programming and statistics

Python is a general-purpose programming language created in the late 1980sand named after Monty Pythonthat's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library.

Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud.

• Get started with data science and Python
• Visualize information
• Wrangle data
• Learn from data
  The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.

Inhalt

Introduction 1

About This Book 1

Foolish Assumptions 3

Icons Used in This Book 4

Beyond the Book 4

Where to Go from Here 5

Part 1: Getting Started With Data Science and Python 7

Chapter 1: Discovering the Match between Data Science and Python 9

Defining the Sexiest Job of the 21st Century 11

Considering the emergence of data science 12

Outlining the core competencies of a data scientist 12

Linking data science, big data, and AI 13

Understanding the role of programming 14

Creating the Data Science Pipeline 14

Preparing the data 15

Performing exploratory data analysis 15

Learning from data 15

Visualizing 15

Obtaining insights and data products 16

Understanding Python's Role in Data Science 16

Considering the shifting profile of data scientists 16

Working with a multipurpose, simple, and efficient language 17

Learning to Use Python Fast 18

Loading data 19

Training a model 19

Viewing a result 19

Chapter 2: Introducing Python's Capabilities and Wonders 21

Why Python? 22

Grasping Python's Core Philosophy 23

Contributing to data science 23

Discovering present and future development goals 24

Working with Python 25

Getting a taste of the language 25

Understanding the need for indentation 26

Working at the command line or in the IDE 27

Performing Rapid Prototyping and Experimentation 31

Considering Speed of Execution 32

Visualizing Power 33

Using the Python Ecosystem for Data Science 35

Accessing scientific tools using SciPy 35

Performing fundamental scientific computing using NumPy 36

Performing data analysis using pandas 36

Implementing machine learning using Scikit-learn 36

Going for deep learning with Keras and TensorFlow 37

Plotting the data using matplotlib 38

Creating graphs with NetworkX 38

Parsing HTML documents using Beautiful Soup 38

Chapter 3: Setting Up Python for Data Science 39

Considering the Off-the-Shelf Cross-Platform Scientific Distributions 40

Getting Continuum Analytics Anaconda 40

Getting Enthought Canopy Express 41

Getting WinPython 42

Installing Anaconda on Windows 42

Installing Anaconda on Linux 46

Installing Anaconda on Mac OS X 47

Downloading the Datasets and Example Code 48

Using Jupyter Notebook 49

Defining the code repository 50

Understanding the datasets used in this book 57

Chapter 4: Working with Google Colab 59

Defining Google Colab 60

Understanding what Google Colab does 60

Considering the online coding difference 61

Using local runtime support 63

Getting a Google Account 63

Creating the account 64

Signing in 64

Working with Notebooks 65

Creating a new notebook 65

Opening existing notebooks 66

Saving notebooks 68

Downloading notebooks 71

Performing Common Tasks 71

Creating code cells 71

Creating text cells 72

Creating special cells 73

Editing cells 74

Moving cells 75

Using Hardware Acceleration 75

Executing the Code 76

Viewing Your Notebook 76

Displaying the table of contents 77

Getting notebook information 77

Checking code execution 78

Sharing Your Notebook 79

Getting Help 80

Part 2: Getting Your Hands Dirty With Data 81

Chapter 5: Understanding the Tools 83 Using the ...