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.

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

Using your Python code in R with reticulate 89

Conducting advanced training using caret 90

Performing machine learning tasks using mlr 90

Visualizing data using ggplot2 91

Enhancing ggplot2 using esquisse 91

Creating graphs with igraph 91

Parsing HTML documents using rvest 92

Wrangling dates using lubridate 92

Making big data simpler using dplyr and purrr 93

Chapter 5: Leveraging a Deep Learning Framework 95

Understanding Deep Learning Framework Usage 96

Working with Low-End Frameworks 97

Chainer 97

PyTorch 98

MXNet 98

Microsoft Cognitive Toolkit/CNTK 99

Understanding TensorFlow 100

Grasping why TensorFlow is so good 101

Making TensorFlow easier by using TFLearn 102

Using Keras as the best simplifier 102

Getting your copy of TensorFlow and Keras 103

Fixing the C++ build tools error in Windows 106

Accessing your new environment in Notebook 108

Book 2: Interacting with Data Storage 109

Chapter 1: Manipulating Raw Data 111

Defining the Data Sources 112

Obtaining data locally 112

Using online data sources 117

Employing dynamic data sources 121

Considering other kinds of data sources 123

Considering the Data Forms 124

Working with pure text 124

Accessing formatted text 125

Deciphering binary data 126

Understanding the Need for Data Reliability 128

Chapter 2: Using Functional Programming Techniques 131

Defining Functional Programming 132

Differences with other programming paradigms 132

Understanding its goals 133

Understanding Pure and Impure Languages 134

Using the pure approach 134

Using the impure approach 134

Comparing the Functional Paradigm 135

Imperative 135

Procedural 136

Object-oriented 136

Declarative 136

Using Python for Functional Programming Needs 137

Understanding How Functional Data Works 138

Working with immutable data 139

Considering the role of state 139

Eliminating side effects 140

Passing by reference versus by value 140

Working with Lists and Strings 142

Creating lists 144

Evaluating lists 144

Performing common list manipulations 146

Understanding the Dict and Set alternatives 147

Considering the use of strings 148

Employing Pattern Matching 150

Looking for patterns in data 150

Understanding regular expressions 152

Using pattern matching in analysis 155

Working with pattern matching 156

Working with Recursion 159

Performing tasks more than once 159

Understanding recursion 161

Using recursion on lists 162

Considering advanced recursive tasks 163

Passing functions instead of variables 164

Performing Functional Data Manipulation 165

Slicing and dicing 166

Mapping your data 167

Filtering data 168

Organizing data 169

Chapter 3: Working with Scalars, Vectors, and Matrices 171

Considering the Data Forms 172

Defining Data Type through Scalars 173

Creating Organized Data with Vectors 174

Defining a vector 175

Creating vectors of a specific type 175

Performing math on vectors 176

Performing logical and comparison tasks on vectors 176

Multiplying vectors 177

Creating and Using Matrices 178

Creating a matrix 178

Creating matrices of a specific type 179

Using the matrix class 181

Performing matrix multiplication 181

Executing advanced matrix operations 183

Extending Analysis to Tensors 185

Using Vectorization Effectively 186

Selecting and Shaping Data 187

Slicing rows 188

Slicing columns 188

Dicing 189

Concatenating 189

Aggregating 194

Working with Trees 195

Understanding the basics of trees 195

Building a tree 196

Representing Relations in a Graph 198

Going beyond trees 198

Arranging graphs 199

Chapter 4: Accessing Data in Files 201

Understanding Flat File Data Sources 202

Working with Positional Data Files 203

Accessing Data in CSV Files 205

Working with a simple CSV file 205

Making use of header information 208

Moving On to XML Files 209

Working with a simple XML file 209

Parsing XML 211

Using XPath for data extraction 212

Considering Other Flat-File Data Sources 214

Working with Nontext Data 215

Downloading Online Datasets 218

Working with package datasets 218

Using public domain datasets 219

Chapter 5: Working with a Relational DBMS 223

Considering RDBMS Issues 224

Defining the use of tables 225

Understanding keys and indexes 226

Using local versus online databases 227

Working in read-only mode 228

Accessing the RDBMS Data 228

Using the SQL language 229

Relying on scripts 231

Relying on views 231

Relying on functions 232

Creating a Dataset 233

Combining data from multiple tables 233

Ensuring data completeness 234

Slicing and dicing the data as needed 234

Mixing RDBMS Products 234

Chapter 6: Working with a NoSQL DMBS 237

Considering the Ramifications of Hierarchical Data 238

Understanding hierarchical organization 238

Developing strategies for freeform data 239

Performing an analysis 240

Working around dangling data 241

Accessing the Data 243

Creating a picture of the data form 243

Employing the correct transiting strategy 244

Ordering the data 247

Interacting with Data from NoSQL Databases 248

Working with Dictionaries 249

Developing Datasets from Hierarchical Data 250

Processing Hierarchical Data into Other Forms 251

Book 3:...