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: Manipulati.