Advanced Data Science and Analytics with Python / Edition 1

by Jesus Rogel-Salazar

Overview

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications.

Features:

• Targets readers with a background in programming, who are interested in the tools used in data analytics and data science
• Uses Python throughout
• Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs
• Focuses on the practical use of the tools rather than on lengthy explanations
• Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path

The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book.

Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app.

About the Author

Dr. Jesús Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

Product Details

ISBN-13:	9781138315068
Publisher:	CRC Press
Publication date:	05/05/2020
Series:	Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Pages:	424
Sales rank:	796,259
Product dimensions:	7.50(w) x 9.25(h) x (d)

About the Author

Dr Jesús Rogel-Salazar is a lead data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones, Barclays and Tympa Health Technologies. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK. He obtained his doctorate in Physics at Imperial College London for work on quantum atom optics and ultra-cold matter.

He has held a position as senior lecturer in mathematics as well as a consultant and data scientist for a number of years in a variety of industries including science, finance, marketing, people analytics and health, among others. He is the author of Data Science and Analytics with Python and Essential Matlab and Octave, both also published with CRC Press. His interests include mathematical modelling, data science and optimisation in a wide range of applications including optics, quantum mechanics, data journalism, finance and health.

Table of Contents

1 No Time to Lose: Time Series Analysis 1

1.1 Time Series 2

1.2 One at a Time: Some Examples 4

1.3 Bearing with Time: Pandas Series 7

1.3.1 Pandas Time Series in Action 18

1.3.2 Time Series Data Manipulation 21

1.4 Modelling Time Series Data 31

1.4.1 Regression… (Not) a Good Idea? 34

1.4.2 Moving Averages and Exponential Smoothing 36

14.3 Stationarity and Seasonality 39

1.4.4 Determining Stationarity 42

1.4.5 Autoregression to the Rescue 48

1.5 Autoregressive Models 51

1.6 Summary 56

2 Speaking Naturally: Text and Natural Language Processing 57

2.1 Pages and Pages: Accessing Data from the Web 59

2.1.1 Beautiful Soup in Action 64

2.2 Make Mine a Regular: Regular Expressions 77

2.2.1 Regular Expression Patterns 79

2.3 Processing Text with Unicode 88

2.4 Tokenising Text 96

2.5 Word Tagging 102

2.6 What Are You Talking About?: Topic Modelling 109

2.6.2 Latent Dirichlet Allocation 110

2.6.2 LDA in Action 115

2.7 Summary 129

3 Getting Social: Graph Theory and Social Network Analysis 131

3.1 Socialising Among Friends and Foes 132

3.2 Let's Make a Connection: Graphs and Networks 140

3.2.1 Taking the Measure: Degree, Centrality and More 145

3.2.2 Connecting the Dots: Network Properties 149

3.3 Social Networks with Python: NetworkX 156

3.3.2 NetworkX: A Quick Intro 156

3.4 Social Network Analysis in Action 162

3.4.2 Karate Kids: Conflict and Fission in a Network 162

3.4.2 In a Galaxy Far, Far Away: Central Characters in a Network 189

3.5 Summary 205

4 Thinking Deeply: Neural Networks and Deep Learning 207

4.1 A Trip Down Memory Lane 208

4.2 No-Brainer: What Are Neural Networks? 214

4.2.1 Neural Network Architecture: Layers and Nodes 215

4.2.2 Firing Away: Neurons, Activate! 218

4.2.3 Going Forwards and Backwards 223

4.3 Neural Networks: From the Ground up 227

4.3.1 Going Forwards 229

4.3.2 Learning the Parameters 232

4.3.3 Backpropagation and Gradient Descent 234

4.3.4 Neural Network: A First Implementation 243

4.4 Neural Networks and Deep Learning 254

4.4.1 Convolutional Neural Networks 263

4.4.2 Convotutional Neural Networks in Action 268

4.4.3 Recurrent Neural Networks 279

4.4.4 Lon8 Short-Term Memory 286

4.4.5 Long Short-Term Memory Networks in Action 290

4.5 Summary 300

5 Here Is One I Made Earlier: Machine Learning Deployment 303

5.1 The Devil in the Detail: Data Products 304

5.2 Apples and Snakes: Core ML + Python 309

5.3 Machine Learning at the Core: Apps and ML 313

5.3.2 Environment Creation 314

5.3.2 Eeny, Meeny, Miny, Moe: Model Selection 315

5.3.3 Location, Location, Location: Exploring the Data 317

5.3.4 Modelling and Core ML: A Crucial Step 322

5.3.5 Model Properties in Core ML 329

5.4 Surprise and Delight: Build an iOS App 331

5.4.1 New Project: Xcode 332

5.4.2 Push My Buttons: Adding Functionality 344

5.4.3 Being Picky: The Picker View 347

5.4.4 Model Behaviour: Core ML + SwiftUI 350

5.5 Summary 355

A Information Criteria 359

B Power Iteration 361

C The Softmax Function and Its Derivative 363

C.1 Numerical Stability 365

D The Derivative of the Cross-Entropy Loss Function 367

Bibliography 369

Index 379