Table Of ContentAdvanced Data Science and
Analytics with Python
Chapman & Hall/CRC
Data Mining and Knowledge Series
Series Editor: Vipin Kumar
Text Mining and Visualization
Case Studies Using Open-Source Tools
Markus Hofmann and Andrew Chisholm
Graph-Based Social Media Analysis
Ioannis Pitas
Data Mining
A Tutorial-Based Primer, Second Edition
Richard J. Roiger
Data Mining with R
Learning with Case Studies, Second Edition
Luís Torgo
Social Networks with Rich Edge Semantics
Quan Zheng and David Skillicorn
Large-Scale Machine Learning in the Earth Sciences
Ashok N. Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser
Data Science and Analytics with Python
Jesús Rogel-Salazar
Feature Engineering for Machine Learning and Data Analytics
Guozhu Dong and Huan Liu
Exploratory Data Analysis Using R
Ronald K. Pearson
Human Capital Systems, Analytics, and Data Mining
Robert C. Hughes
Industrial Applications of Machine Learning
Pedro Larrañaga et al
Automated Data Analysis Using Excel
Second Edition
Brian D. Bissett
Advanced Data Science and Analytics with Python
Jesús Rogel-Salazar
For more information about this series please visit:
https://www.crcpress.com/Chapman--HallCRC-Data-Mining-and-Knowledge-Discovery-Series/book-
series/CHDAMINODIS
Advanced Data Science and
Analytics with Python
Jesús Rogel-Salazar
First edition published 2020
by CRC Press
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and by CRC Press
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ISBN: 978-0-429-44661-0 (hbk)
ISBN: 978-1-138-31506-8 (pbk)
ISBN: 978-0-429-44664-1 (ebk)
To A. J. Johnson
Then. Now. Always.
Contents
1
1 No Time to Lose: Time Series Analysis
2
1.1 Time Series
4
1.2 One at a Time: Some Examples
7
1.3 Bearing with Time: Pandas Series
1.3.1 Pandas Time Series in Action 18
1.3.2 Time Series Data Manipulation 21
31
1.4 Modelling Time Series Data
1.4.1 Regression... (Not) a Good Idea? 34
1.4.2 Moving Averages and Exponential Smoothing 36
1.4.3 Stationarity and Seasonality 39
1.4.4 Determining Stationarity 42
1.4.5 Autoregression to the Rescue 48
51
1.5 Autoregressive Models
56
1.6 Summary
viii j. rogel-salazar
57
2 Speaking Naturally: Text and Natural Language Processing
59
2.1 Pages and Pages: Accessing Data from the Web
2.1.1 Beautiful Soup in Action 64
77
2.2 Make Mine a Regular: Regular Expressions
2.2.1 Regular Expression Patterns 79
88
2.3 Processing Text with Unicode
96
2.4 Tokenising Text
102
2.5 Word Tagging
109
2.6 What Are You Talking About?: Topic Modelling
2.6.1 Latent Dirichlet Allocation 110
2.6.2 LDA in Action 115
129
2.7 Summary
131
3 Getting Social: Graph Theory and Social Network Analysis
132
3.1 Socialising Among Friends and Foes
140
3.2 Let’s Make a Connection: Graphs and Networks
3.2.1 Taking the Measure: Degree, Centrality and More 145
3.2.2 Connecting the Dots: Network Properties 149
156
3.3 Social Networks with Python: NetworkX
3.3.1 NetworkX: A Quick Intro 156
162
3.4 Social Network Analysis in Action
3.4.1 Karate Kids: Conflict and Fission in a Network 162
3.4.2 In a Galaxy Far, Far Away: Central Characters in a Network 189
advanced data science and analytics with python ix
205
3.5 Summary
207
4 Thinking Deeply: Neural Networks and Deep Learning
208
4.1 A Trip Down Memory Lane
214
4.2 No-Brainer: What Are Neural Networks?
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
227
4.3 Neural Networks: From the Ground up
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
254
4.4 Neural Networks and Deep Learning
4.4.1 Convolutional Neural Networks 263
4.4.2 Convolutional Neural Networks in Action 268
4.4.3 Recurrent Neural Networks 279
4.4.4 Long Short-Term Memory 286
4.4.5 Long Short-Term Memory Networks in Action 290
300
4.5 Summary
