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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.
Auteur
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.
Texte du rabat
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...
Résumé
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.
Contenu
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 ...