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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