Preface xvii
Before You Begin xxxiii
Chapter 1: Introduction to Computers and Python 1
1.1 Introduction 2
1.2 A Quick Review of Object Technology Basics 3
1.3 Python 5
1.4 It's the Libraries! 7
1.5 Test-Drives: Using IPython and Jupyter Notebooks 9
1.6 The Cloud and the Internet of Things 16
1.7 How Big Is Big Data? 17
1.8 Case Study-A Big-Data Mobile Application 24
1.9 Intro to Data Science: Artificial Intelligence-at the Intersection of CS and Data Science 26
1.10 Wrap-Up 29
Chapter 2: Introduction to Python Programming 31
2.1 Introduction 32
2.2 Variables and Assignment Statements 32
2.3 Arithmetic 33
2.4 Function print and an Intro to Single- and Double-Quoted Strings 36
2.5 Triple-Quoted Strings 38
2.6 Getting Input from the User 39
2.7 Decision Making: The if Statement and Comparison Operators 41
2.8 Objects and Dynamic Typing 45
2.9 Intro to Data Science: Basic Descriptive Statistics 46
2.10 Wrap-Up 48
Chapter 3: Control Statements 49
3.1 Introduction 50
3.2 Control Statements 50
3.3 if Statement 51
3.4 if…else and if…elif…else Statements 52
3.5 while Statement 55
3.6 for Statement 55
3.7 Augmented Assignments 57
3.8 Sequence-Controlled Iteration; Formatted Strings 58
3.9 Sentinel-Controlled Iteration 59
3.10 Built-In Function range: A Deeper Look 60
3.11 Using Type Decimal for Monetary Amounts 61
3.12 break and continue Statements 64
3.13 Boolean Operators and, or and not 65
3.14 Intro to Data Science: Measures of Central Tendency-Mean, Median and Mode 67
3.15 Wrap-Up 69
Chapter 4: Functions 71
4.1 Introduction 72
4.2 Defining Functions 72
4.3 Functions with Multiple Parameters 75
4.4 Random-Number Generation 76
4.5 Case Study: A Game of Chance 78
4.6 Python Standard Library 81
4.7 math Module Functions 82
4.8 Using IPython Tab Completion for Discovery 83
4.9 Default Parameter Values 85
4.10 Keyword Arguments 85
4.11 Arbitrary Argument Lists 86
4.12 Methods: Functions That Belong to Objects 87
4.13 Scope Rules 87
4.14 import: A Deeper Look 89
4.15 Passing Arguments to Functions: A Deeper Look 90
4.16 Recursion 93
4.17 Functional-Style Programming 95
4.18 Intro to Data Science: Measures of Dispersion 97
4.19 Wrap-Up 98
Chapter 5: Sequences: Lists and Tuples 101
5.1 Introduction 102
5.2 Lists 102
5.3 Tuples 106
5.4 Unpacking Sequences 108
5.5 Sequence Slicing 110
5.6 del Statement 112
5.7 Passing Lists to Functions 113
5.8 Sorting Lists 115
5.9 Searching Sequences 116
5.10 Other List Methods 117
5.11 Simulating Stacks with Lists 119
5.12 List Comprehensions 120
5.13 Generator Expressions 121
5.14 Filter, Map and Reduce 122
5.15 Other Sequence Processing Functions 124
5.16 Two-Dimensional Lists 126
5.17 Intro to Data Science: Simulation and Static Visualizations 128
5.18 Wrap-Up 135
Chapter 6: Dictionaries and Sets 137
6.1 Introduction 138
6.2 Dictionaries 138
6.3 Sets 147
6.4 Intro to Data Science: Dynamic Visualizations 152
6.5 Wrap-Up 158
Chapter 7: Array-Oriented Programming with NumPy 159
7.1 Introduction 160
7.2 Creating arrays from Existing Data 160
7.3 array Attributes 161
7.4 Filling arrays with Specific Values 163
7.5 Creating arrays from Ranges 164
7.6 List vs. array Performance: Introducing %timeit 165
7.7 array Operators 167
7.8 NumPy Calculation Methods 169
7.9 Universal Functions 170
7.10 Indexing and Slicing 171
7.11 Views: Shallow Copies 173
7.12 Deep Copies 174
7.13 Reshaping and Transposing 175
7.14 Intro to Data Science: pandas Series and DataFrames 177
7.15 Wrap-Up 189
Chapter 8: Strings: A Deeper Look 191
8.1 Introduction 192
8.2 Formatting Strings 193
8.3 Concatenating and Repeating Strings 196
8.4 Stripping Whitespace from Strings 197
8.5 Changing Character Case 197
8.6 Comparison Operators for Strings 198
8.7 Searching for Substrings 198
8.8 Replacing Substrings 199
8.9 Splitting and Joining Strings 200
8.10 Characters and Character-Testing Methods 202
8.11 Raw Strings 203
8.12 Introduction to Regular Expressions 203
8.13 Intro to Data Science: Pandas, Regular Expressions and Data Munging 210
8.14 Wrap-Up 214
Chapter 9: Files and Exceptions 217
9.1 Introduction 218
9.2 Files 219
9.3 Text-File Processing 219
9.4 Updating Text Files 222
9.5 Serialization with JSON 223
9.6 Focus on Security: pickle Serialization and Deserialization 226
9.7 Additional Notes Regarding Files 226
9.8 Handling Exceptions 227
9.9 finally Clause 231
9.10 Explicitly Raising an Exception 233
9.11 (Optional) Stack Unwinding and Tracebacks 233
9.12 Intro to Data Science: Working with CSV Files 235
9.13 Wrap-Up 241
Chapter 10: Object-Oriented Programming 243
10.1 Introduction 244
10.2 Custom Class Account 246
10.3 Controlling Access to Attributes 249
10.4 Properties for Data Access 250
10.5 Simulating "Private" Attributes 256
10.6 Case Study: Card Shuffling and Dealing Simulation 258
10.7 Inheritance: Base Classes and Subclasses 266
10.8 Building an Inheritance Hierarchy; Introducing Polymorphism 267
10.9 Duck Typing and Polymorphism 275
10.10 Operator Overloading 276
10.11 Exception Class Hierarchy and Custom Exceptions 279
10.12 Named Tuples 280
10.13 A Brief Intro to Python 3.7's New Data Classes 281
10.14 Unit Testing with Docstrings and doctest 287
10.15 Namespaces and Scopes 290
10.16 Intro to Data Science: Time Series and Simple Linear Regression 293
10.17 Wrap-Up 301
Chapter 11: Natural Language Processing (NLP) 303
11.1 Introduction 304
11.2 TextBlob 305
11.3 Visualizing Word Frequencies with Bar Charts and Word Clouds 319
11.4 Readability Assessment with Textatistic 324
11.5 Named Entity Recognition with spaCy 326
11.6 Similarity Detection with spaCy 327
11.7 Other NLP Libraries and Tools 328
11.8 Machine Learning and Deep Learning Natural Language Applications 328
11.9 Natural Language Datasets 329
11.10 Wrap-Up 330
Chapter 12: Data Mining Twitter 331
12.1 Introduction 332
12.2 Overview of the Twitter APIs 334
12.3 Creating a Twitter Account 335
12.4 Getting Twitter Credentials-Creating an App 335
12.5 What's in a Tweet? 337
12.6 Tweepy 340
12.7 Authenticating with Twitter Via Tweepy 341
12.8 Getting Information About a Twitter Account 342
12.9 Introduction to Tweepy Cursors: Getting an Account's Followers and Friends 344
12.10 Searching Recent Tweets 347
12.11 Spotting Trends: Twitter Trends API 349
12.12 Cleaning/Preprocessing Tweets for Analysis 353
12.13 Twitter Streaming API 354
12.14 Tweet Sentiment Analysis 359
12.15 Geocoding and Mapping 362
12.16 Ways to Store Tweets 370
12.17 Twitter and Time Series 370
12.18 Wrap-Up 371
Chapter 13: IBM Watson and Cognitive Computing 373
13.1 Introduction: IBM Watson and Cognitive Computing 374
13.2 IBM Cloud Account and Cloud Console 375
13.3 Watson Services 376
13.4 Additional Services and Tools 379
13.5 Watson Developer Cloud Python SDK 381
13.6 Case Study: Traveler's Companion Translation App 381
13.7 Watson Resources 394
13.8 Wrap-Up 395
Chapter 14: Machine Learning: Classification, Regression and Clustering 397
14.1 Introduction to Machine Learning 398
14.2 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1 403
14.3 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2 413
14.4 Case Study: Time Series and Simple Linear Regression 420
14.5 Case Study: Multiple Linear Regression with the California Housing Dataset 425
14.6 Case Study: Unsupervised Machine Learning, Part 1-Dimensionality Reduction 438
14.7 Case Study: Unsupervised Machine Learning, Part 2-k-Means Clustering 442
14.8 Wrap-Up 455
Chapter 15: Deep Learning 457
15.1 Introduction 458
15.2 Keras Built-In Datasets 461
15.3 Custom Anaconda Environments 462
15.4 Neural Networks 463
15.5 Tensors 465
15.6 Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset 467
15.7 Visualizing Neural Network Training with TensorBoard 486
15.8 ConvnetJS: Browser-Based Deep-Learning Training and Visualization 489
15.9 Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset 489
15.10 Tuning Deep Learning Models 497
15.11 Convnet Models Pretrained on ImageNet 498
15.12 Wrap-Up 499
Chapter 16: Big Data: Hadoop, Spark, NoSQL and IoT 501
16.1 Introduction 502
16.2 Relational Databases and Structured Query Language (SQL) 506
16.3 NoSQL and NewSQL Big-Data Databases: A Brief Tour 517
16.4 Case Study: A MongoDB JSON Document Database 520
16.5 Hadoop 530
16.6 Spark 541
16.7 Spark Streaming: Counting Twitter Hashtags Using the pyspark-notebook Docker Stack 551
16.8 Internet of Things and Dashboards 560
16.9 Wrap-Up 571
Index 573
Preface xvii
Before You Begin xxxiii
Chapter 1: Introduction to Computers and Python 1
1.1 Introduction 2
1.2 A Quick Review of Object Technology Basics 3
1.3 Python 5
1.4 It's the Libraries! 7
1.5 Test-Drives: Using IPython and Jupyter Notebooks 9
1.6 The Cloud and the Internet of Things 16
1.7 How Big Is Big Data? 17
1.8 Case Study-A Big-Data Mobile Application 24
1.9 Intro to Data Science: Artificial Intelligence-at the Intersection of CS and Data Science 26
1.10 Wrap-Up 29
Chapter 2: Introduction to Python Programming 31
2.1 Introduction 32
2.2 Variables and Assignment Statements 32
2.3 Arithmetic 33
2.4 Function print and an Intro to Single- and Double-Quoted Strings 36
2.5 Triple-Quoted Strings 38
2.6 Getting Input from the User 39
2.7 Decision Making: The if Statement and Comparison Operators 41
2.8 Objects and Dynamic Typing 45
2.9 Intro to Data Science: Basic Descriptive Statistics 46
2.10 Wrap-Up 48
Chapter 3: Control Statements 49
3.1 Introduction 50
3.2 Control Statements 50
3.3 if Statement 51
3.4 if…else and if…elif…else Statements 52
3.5 while Statement 55
3.6 for Statement 55
3.7 Augmented Assignments 57
3.8 Sequence-Controlled Iteration; Formatted Strings 58
3.9 Sentinel-Controlled Iteration 59
3.10 Built-In Function range: A Deeper Look 60
3.11 Using Type Decimal for Monetary Amounts 61
3.12 break and continue Statements 64
3.13 Boolean Operators and, or and not 65
3.14 Intro to Data Science: Measures of Central Tendency-Mean, Median and Mode 67
3.15 Wrap-Up 69
Chapter 4: Functions 71
4.1 Introduction 72
4.2 Defining Functions 72
4.3 Functions with Multiple Parameters 75
4.4 Random-Number Generation 76
4.5 Case Study: A Game of Chance 78
4.6 Python Standard Library 81
4.7 math Module Functions 82
4.8 Using IPython Tab Completion for Discovery 83
4.9 Default Parameter Values 85
4.10 Keyword Arguments 85
4.11 Arbitrary Argument Lists 86
4.12 Methods: Functions That Belong to Objects 87
4.13 Scope Rules 87
4.14 import: A Deeper Look 89
4.15 Passing Arguments to Functions: A Deeper Look 90
4.16 Recursion 93
4.17 Functional-Style Programming 95
4.18 Intro to Data Science: Measures of Dispersion 97
4.19 Wrap-Up 98
Chapter 5: Sequences: Lists and Tuples 101
5.1 Introduction 102
5.2 Lists 102
5.3 Tuples 106
5.4 Unpacking Sequences 108
5.5 Sequence Slicing 110
5.6 del Statement 112
5.7 Passing Lists to Functions 113
5.8 Sorting Lists 115
5.9 Searching Sequences 116
5.10 Other List Methods 117
5.11 Simulating Stacks with Lists 119
5.12 List Comprehensions 120
5.13 Generator Expressions 121
5.14 Filter, Map and Reduce 122
5.15 Other Sequence Processing Functions 124
5.16 Two-Dimensional Lists 126
5.17 Intro to Data Science: Simulation and Static Visualizations 128
5.18 Wrap-Up 135
Chapter 6: Dictionaries and Sets 137
6.1 Introduction 138
6.2 Dictionaries 138
6.3 Sets 147
6.4 Intro to Data Science: Dynamic Visualizations 152
6.5 Wrap-Up 158
Chapter 7: Array-Oriented Programming with NumPy 159
7.1 Introduction 160
7.2 Creating arrays from Existing Data 160
7.3 array Attributes 161
7.4 Filling arrays with Specific Values 163
7.5 Creating arrays from Ranges 164
7.6 List vs. array Performance: Introducing %timeit 165
7.7 array Operators 167
7.8 NumPy Calculation Methods 169
7.9 Universal Functions 170
7.10 Indexing and Slicing 171
7.11 Views: Shallow Copies 173
7.12 Deep Copies 174
7.13 Reshaping and Transposing 175
7.14 Intro to Data Science: pandas Series and DataFrames 177
7.15 Wrap-Up 189
Chapter 8: Strings: A Deeper Look 191
8.1 Introduction 192
8.2 Formatting Strings 193
8.3 Concatenating and Repeating Strings 196
8.4 Stripping Whitespace from Strings 197
8.5 Changing Character Case 197
8.6 Comparison Operators for Strings 198
8.7 Searching for Substrings 198
8.8 Replacing Substrings 199
8.9 Splitting and Joining Strings 200
8.10 Characters and Character-Testing Methods 202
8.11 Raw Strings 203
8.12 Introduction to Regular Expressions 203
8.13 Intro to Data Science: Pandas, Regular Expressions and Data Munging 210
8.14 Wrap-Up 214
Chapter 9: Files and Exceptions 217
9.1 Introduction 218
9.2 Files 219
9.3 Text-File Processing 219
9.4 Updating Text Files 222
9.5 Serialization with JSON 223
9.6 Focus on Security: pickle Serialization and Deserialization 226
9.7 Additional Notes Regarding Files 226
9.8 Handling Exceptions 227
9.9 finally Clause 231
9.10 Explicitly Raising an Exception 233
9.11 (Optional) Stack Unwinding and Tracebacks 233
9.12 Intro to Data Science: Working with CSV Files 235
9.13 Wrap-Up 241
Chapter 10: Object-Oriented Programming 243
10.1 Introduction 244
10.2 Custom Class Account 246
10.3 Controlling Access to Attributes 249
10.4 Properties for Data Access 250
10.5 Simulating "Private" Attributes 256
10.6 Case Study: Card Shuffling and Dealing Simulation 258
10.7 Inheritance: Base Classes and Subclasses 266
10.8 Building an Inheritance Hierarchy; Introducing Polymorphism 267
10.9 Duck Typing and Polymorphism 275
10.10 Operator Overloading 276
10.11 Exception Class Hierarchy and Custom Exceptions 279
10.12 Named Tuples 280
10.13 A Brief Intro to Python 3.7's New Data Classes 281
10.14 Unit Testing with Docstrings and doctest 287
10.15 Namespaces and Scopes 290
10.16 Intro to Data Science: Time Series and Simple Linear Regression 293
10.17 Wrap-Up 301
Chapter 11: Natural Language Processing (NLP) 303
11.1 Introduction 304
11.2 TextBlob 305
11.3 Visualizing Word Frequencies with Bar Charts and Word Clouds 319
11.4 Readability Assessment with Textatistic 324
11.5 Named Entity Recognition with spaCy 326
11.6 Similarity Detection with spaCy 327
11.7 Other NLP Libraries and Tools 328
11.8 Machine Learning and Deep Learning Natural Language Applications 328
11.9 Natural Language Datasets 329
11.10 Wrap-Up 330
Chapter 12: Data Mining Twitter 331
12.1 Introduction 332
12.2 Overview of the Twitter APIs 334
12.3 Creating a Twitter Account 335
12.4 Getting Twitter Credentials-Creating an App 335
12.5 What's in a Tweet? 337
12.6 Tweepy 340
12.7 Authenticating with Twitter Via Tweepy 341
12.8 Getting Information About a Twitter Account 342
12.9 Introduction to Tweepy Cursors: Getting an Account's Followers and Friends 344
12.10 Searching Recent Tweets 347
12.11 Spotting Trends: Twitter Trends API 349
12.12 Cleaning/Preprocessing Tweets for Analysis 353
12.13 Twitter Streaming API 354
12.14 Tweet Sentiment Analysis 359
12.15 Geocoding and Mapping 362
12.16 Ways to Store Tweets 370
12.17 Twitter and Time Series 370
12.18 Wrap-Up 371
Chapter 13: IBM Watson and Cognitive Computing 373
13.1 Introduction: IBM Watson and Cognitive Computing 374
13.2 IBM Cloud Account and Cloud Console 375
13.3 Watson Services 376
13.4 Additional Services and Tools 379
13.5 Watson Developer Cloud Python SDK 381
13.6 Case Study: Traveler's Companion Translation App 381
13.7 Watson Resources 394
13.8 Wrap-Up 395
Chapter 14: Machine Learning: Classification, Regression and Clustering 397
14.1 Introduction to Machine Learning 398
14.2 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1 403
14.3 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2 413
14.4 Case Study: Time Series and Simple Linear Regression 420
14.5 Case Study: Multiple Linear Regression with the California Housing Dataset 425
14.6 Case Study: Unsupervised Machine Learning, Part 1-Dimensionality Reduction 438
14.7 Case Study: Unsupervised Machine Learning, Part 2-k-Means Clustering 442
14.8 Wrap-Up 455
Chapter 15: Deep Learning 457
15.1 Introduction 458
15.2 Keras Built-In Datasets 461
15.3 Custom Anaconda Environments 462
15.4 Neural Networks 463
15.5 Tensors 465
15.6 Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset 467
15.7 Visualizing Neural Network Training with TensorBoard 486
15.8 ConvnetJS: Browser-Based Deep-Learning Training and Visualization 489
15.9 Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset 489
15.10 Tuning Deep Learning Models 497
15.11 Convnet Models Pretrained on ImageNet 498
15.12 Wrap-Up 499
Chapter 16: Big Data: Hadoop, Spark, NoSQL and IoT 501
16.1 Introduction 502
16.2 Relational Databases and Structured Query Language (SQL) 506
16.3 NoSQL and NewSQL Big-Data Databases: A Brief Tour 517
16.4 Case Study: A MongoDB JSON Document Database 520
16.5 Hadoop 530
16.6 Spark 541
16.7 Spark Streaming: Counting Twitter Hashtags Using the pyspark-notebook Docker Stack 551
16.8 Internet of Things and Dashboards 560
16.9 Wrap-Up 571
Index 573
Paul Deitel, CEO and Chief Technical Officer of Deitel & Associates, Inc., is a graduate of MIT, where he studied Information Technology. Through Deitel & Associates, Inc., he has delivered hundreds of programming courses worldwide to clients, including Cisco, IBM, Siemens, Sun Microsystems, Dell, Fidelity, NASA at the Kennedy Space Center, the National Severe Storm Laboratory, White Sands Missile Range, Rogue Wave Software, Boeing, SunGard Higher Education, Nortel Networks, Puma, iRobot, Invensys and many more. He and his co-author, Dr. Harvey M. Deitel, are the world’s best-selling programming-language textbook/professional book/video authors. Dr. Harvey Deitel, Chairman and Chief Strategy Officer of Deitel & Associates, Inc., has over 50 years of experience in the computer field. Dr. Deitel earned B.S. and M.S. degrees in Electrical Engineering from MIT and a Ph.D. in Mathematics from Boston University. He has extensive college teaching experience, including earning tenure and serving as the Chairman of the Computer Science Department at Boston College before founding Deitel & Associates, Inc., in 1991 with his son, Paul. The Deitels’ publications have earned international recognition, with translations published in Japanese, German, Russian, Spanish, French, Polish, Italian, Simplified Chinese, Traditional Chinese, Korean, Portuguese, Greek, Urdu and Turkish. Dr. Deitel has delivered hundreds of programming courses to corporate, academic, government and military clients.
“The chapters are clearly written with detailed explanations of the
example code. The modular structure, wide range of contemporary
data science topics, and code in companion Jupyter notebooks make
this a fantastic resource for readers of a variety of backgrounds.
Fabulous Big Data chapter—it covers all of the relevant programs
and platforms. Great Watson chapter! The chapter provides a great
overview of the Watson applications. Also, your translation
examples are great because they provide an ‘instant reward’—it’s
very satisfying to implement a task and receive results so quickly.
Machine Learning is a huge topic, and the chapter serves as a great
introduction. I loved the California housing data example—very
relevant for business analytics. The chapter was visually
stunning.”
—Alison Sanchez, Assistant Professor in Economics, University of
San Diego
“A great introduction to Big Data concepts, notably Hadoop, Spark,
and IoT. The examples are extremely realistic and practical. The
authors do an excellent job of combining programming and data
science topics. The material is presented in digestible sections
accompanied by engaging interactive examples. Nearly all concepts
are accompanied by a worked-out example. A comprehensive overview
of object-oriented programming in Python—the use of card image
graphics is sure to engage the reader.”
—Garrett Dancik, Eastern Connecticut State University
“Covers some of the most modern Python syntax approaches and
introduces community standards for style and documentation. The
machine learning chapter does a great job of walking people through
the boilerplate code needed for ML in Python. The case studies
accomplish this really well. The later examples are so visual. Many
of the model evaluation tasks make for really good programming
practice. I can see readers feeling really excited about playing
with the animations.”
—Elizabeth Wickes, Lecturer, School of Information Sciences,
University of Illinois at Urbana-Champaign
“An engaging, highly accessible book that will foster curiosity and
motivate beginning data scientists to develop essential foundations
in Python programming, statistics, data manipulation, working with
APIs, data visualization, machine learning, cloud computing, and
more. Great walkthrough of the Twitter APIs—sentiment analysis
piece is very useful. I’ve taken several classes that cover natural
language processing and this is the first time the tools and
concepts have been explained so clearly. I appreciate the
discussion of serialization with JSON and pickling and when to use
one or the other—with an emphasis on using JSON over pickle—good to
know there’s a better, safer way!”
—Jamie Whitacre, Data Science Consultant
“For a while, I have been looking for a book in Data Science using
Python that would cover the most relevant technologies. Well, my
search is over. A must-have book for any practitioner of this
field. The machine learning chapter is a real winner!! The dynamic
visualization is fantastic.”
—Ramon Mata-Toledo, Professor, James Madison University
“I like the new combination of topics from computer science, data
science, and stats. This is important for building data science
programs that are more than just cobbling together math and
computer science courses. A book like this may help facilitate
expanding our offerings and using Python as a bridge for computer
and data science topics. For a data science program that focuses on
a single language (mostly), I think Python is probably the way to
go.”
—Lance Bryant, Shippensburg University
“You’ll develop applications using industry standard libraries and
cloud computing services.”
—Daniel Chen, Data Scientist, Lander Analytics
“Great introduction to Python! This book has my strongest
recommendation both as an introduction to Python as well as Data
Science.”
—Shyamal Mitra, Senior Lecturer, University of Texas
“IBM Watson is an exciting chapter. The code examples put together
a lot of Watson services in a really nifty example.”
—Daniel Chen, Data Scientist, Lander Analytics
“Fun, engaging real-world examples will encourage readers to
conduct meaningful data analyses. Provides many of the best
explanations of data science concepts I’ve encountered. Introduces
the most useful starter machine learning models—does a good job
explaining how to choose the best model and what ‘the best’ means.
Great overview of all the big data technologies with relevant
examples.”
—Jamie Whitacre, Data Science Consultant
“A great introduction to deep learning.”
—Alison Sanchez, University of San Diego
“The best designed Intro to Data Science/Python book I have
seen.”
—Roland DePratti, Central Connecticut State University
“I like the new combination of topics from computer science, data
science, and stats.”
—Lance Bryant, Shippensburg University
“The book’s applied approach should engage readers. A fantastic job
providing background on various machine learning concepts without
burdening the users with too many mathematical details.”
—Garrett Dancik, Assoc. Prof. of Computer Science/Bioinformatics,
Eastern Connecticut State University
“Helps readers leverage the large number of existing libraries to
accomplish tasks with minimal code. Concepts are accompanied by
rich Python examples that readers can adapt to implement their own
solutions to data science problems. I like that cloud services are
used.”
—David Koop, Assistant Professor, U-Mass Dartmouth
“I enjoyed the OOP chapter—doctest unit testing is nice because you
can have the test in the actual docstring so things are traveling
together. The line-by-line explanations of the static and dynamic
visualizations of the die rolling example are just great.”
—Daniel Chen, Data Scientist, Lander Analytics
“A lucid exposition of the fundamentals of Python and Data Science.
Thanks for pointing out seeding the random number generator for
reproducibility. I like the use of dictionary and set
comprehensions for succinct programming. ‘List vs. Array
Performance: Introducing %timeit’ is convincing on why one should
use ndarrays. Good defensive programming. Great section on Pandas
Series and DataFrames—one of the clearest expositions that I have
seen. The section on data wrangling is excellent. Natural Language
Processing is an excellent chapter! I learned a tremendous amount
going through it.”
—Shyamal Mitra, Senior Lecturer, University of Texas
“I like the discussion of exceptions and tracebacks. I really liked
the Data Mining Twitter chapter; it focused on a real data source
and brought in a lot of techniques for analysis (e.g.,
visualization, NLP). I like that the Python modules helped hide
some of the complexity. Word clouds look cool.”
—David Koop, Assistant Professor, U-Mass Dartmouth
“I love the book! The examples are definitely a high point.”
—Dr. Irene Bruno, George Mason University
“I was very excited to see this book. I like its focus on data
science and a general purpose language for writing useful data
science programs. The data science portion distinguishes this book
from most other introductory Python books.”
—Dr. Harvey Siy, University of Nebraska at Omaha
“I’ve learned a lot in this review process, discovering the
exciting field of AI. I’ve liked the Deep Learning chapter, which
has left me amazed with the things that have already been achieved
in this field.”
—José Antonio González Seco, Consultant
“An impressive hands-on approach to programming meant for
exploration and experimentation.”
—Elizabeth Wickes, Lecturer, School of Information Sciences,
University of Illinois at Urbana-Champaign
“I was impressed at how easy it was to get started with NLP using
Python. A meaningful overview of deep learning concepts, using
Keras. I like the streaming example.”
—David Koop, Assistant Professor, U-Mass Dartmouth
“Really like the use of f-strings, instead of the older
string-formatting methods. Seeing how easy TextBlob is compared to
base NLTK was great. I never made word clouds with shapes before,
but I can see this being a motivating example for people getting
started with NLP. I’m enjoying the case-study chapters in the
latter parts of the book. They are really practical. I really
enjoyed working through all the Big Data examples, especially the
IoT ones.”
—Daniel Chen, Data Scientist, Lander Analytics
“I really liked the live IPython input-output. The thing that I
like most about this product is that it is a Deitel & Deitel book
(I’m a big fan) that covers Python.”
—Dr. Mark Pauley, University of Nebraska at Omaha
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