Python for Data Science For Dummies (häftad)
Häftad (Paperback / softback)
Antal sidor
2nd Edition
John Wiley & Sons Inc
Black & white illustrations
234 x 188 x 28 mm
658 g
Antal komponenter
3:B&W 7.5 x 9.25 in or 235 x 191 mm Perfect Bound on White w/Gloss Lam
Python for Data Science For Dummies (häftad)

Python for Data Science For Dummies

Häftad Engelska, 2019-04-05
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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.
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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 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.


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 Jupyter Console 84 Interacting with screen text 84 Changing the window appearance 86 Getting Python help 87 Getting IPython help 89 Using magic functions 90 Discovering objects 91 Using Jupyter Notebook 93 Working with styles 93 Restarting the kernel 94 Restoring a checkpoint 95 Performing Multimedia and Graphic Integration 96 Embedding plots and other images 96 Loading examples from online sites 96 Obtaining online graphics and multimedia 96 Chapter 6: Working with Real Data 99 Uploading, Streaming, and Sampling Data 100 Uploading small amounts of data into memory 101 Streaming large amounts of data into memory 102 Generating variations on image data 103 Sampling data in different ways 104 Accessing Data in Structured Fla