Data Science Programming All-in-One For Dummies - John Paul Mueller

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Auteur(s) : John Paul Mueller - Luca Massaron

Editeur : Wiley John + Sons

Langue : Anglais

Parution : 01/02/2020

Format : Moyen, de 350g à 1kg

Nombre de pages : 768

Expédition : 1411

Dimensions : 23.6 x 18.7 x 4.5

ISBN : 1119626110

Résumé :

Your logical, linear guide to the fundamentals of data science programming

Data science is exploding-in a good way-with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models.

Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time.

• Get grounded: the ideal start for new data professionals
• What lies ahead: learn about specific areas that data is transforming ?
• Be meaningful: find out how to tell your data story
• See clearly: pick up the art of visualization

Whether you're a beginning student or already mid-career, get your copy now and add even more meaning to your life-and everyone else's!

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Biographie:

John Mueller has produced 114 books and more than 600 articles on topics ranging from functional programming techniques to working with Amazon Web Services (AWS). Luca Massaron, a Google Developer Expert (GDE),??interprets big data and transforms it into smart data through simple and effective data mining and machine learning techniques....

Sommaire:

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

Book 1: Defining Data Science 7

Chapter 1: Considering the History and Uses of Data Science 9

Considering the Elements of Data Science 10

Considering the emergence of data science 10

Outlining the core competencies of a data scientist 11

Linking data science, big data, and AI 12

Understanding the role of programming 12

Defining the Role of Data in the World 13

Enticing people to buy products 13

Keeping people safer 14

Creating new technologies 15

Performing analysis for research 16

Providing art and entertainment 17

Making life more interesting in other ways 18

Creating the Data Science Pipeline 18

Preparing the data 18

Performing exploratory data analysis 18

Learning from data 19

Visualizing 19

Obtaining insights and data products 19

Comparing Different Languages Used for Data Science 20

Obtaining an overview of data science languages 20

Defining the pros and cons of using Python 22

Defining the pros and cons of using R 23

Learning to Perform Data Science Tasks Fast 25

Loading data 26

Training a model 26

Viewing a result 26

Chapter 2: Placing Data Science within the Realm of AI 29

Seeing the Data to Data Science Relationship 30

Considering the data architecture 30

Acquiring data from various sources 31

Performing data analysis 32

Archiving the data 33

Defining the Levels of AI 33

Beginning with AI 34

Advancing to machine learning 39

Getting detailed with deep learning 43

Creating a Pipeline from Data to AI 47

Considering the desired output 47

Defining a data architecture 47

Combining various data sources 47

Checking for errors and fixing them 48

Performing the analysis 48

Validating the result 49

Enhancing application performance 49

Chapter 3: Creating a Data Science Lab of Your Own 51

Considering the Analysis Platform Options 52

Using a desktop system 53

Working with an online IDE 53

Considering the need for a GPU 54

Choosing a Development Language 56

Obtaining and Using Python 58

Working with Python in this book 58

Obtaining and installing Anaconda for Python 59

Defining a Python code repository 64

Working with Python using Google Colaboratory 69

Defining the limits of using Azure Notebooks with Python and R 71

Obtaining and Using R 72

Obtaining and installing Anaconda for R 72

Starting the R environment 73

Defining an R code repository 75

Presenting Frameworks 76

Defining the differences 76

Explaining the popularity of frameworks 77

Choosing a particular library 79

Accessing the Downloadable Code 80

Chapter 4: Considering Additional Packages and Libraries You Might Want 81

Considering the Uses for Third-Party Code 82

Obtaining Useful Python Packages 83

Accessing scientific tools using SciPy 84

Performing fundamental scientific computing using NumPy 85

Performing data analysis using pandas 85

Implementing machine learning using Scikit-learn 86

Going for deep learning with Keras and TensorFlow 86

Plotting the data using matplotlib 87

Creating graphs with NetworkX 88

Parsing HTML documents using Beautiful Soup 88

Locating Useful R Libraries 89