Paperback : £82.22
About our authors
Paul J. Deitel, CEO and Chief Technical Officer of Deitel & Associates, Inc., is an MIT graduate with 43 years in computing. He is one of the world's most experienced programming-languages trainers, having taught professional courses to software developers since 1992. He has delivered hundreds of programming courses to academic, industry, government and military clients of Deitel & Associates, Inc. internationally, including UCLA, SLB (formerly Schlumberger), Cisco, IBM, Siemens, Sun Microsystems (now Oracle), Dell, Fidelity, NASA at the Kennedy Space Center, the National Severe Storm Laboratory, White Sands Missile Range, Rogue Wave Software, Boeing, Puma, iRobot and many more.
Dr. Harvey M. Deitel, Chairman and Chief Strategy Officer of Deitel & Associates, Inc., has 62 years of experience in computing. Dr. Deitel earned B.S. and M.S. degrees in Electrical Engineering from MIT and a Ph.D. in Mathematics from Boston University; he studied computing in each of these programs before they spun off Computer Science departments. He has extensive college and professional teaching experience, including earning tenure and serving as the Chairman of the Computer Science Department at Boston College before founding Deitel & Associates in 1991 with his son, Paul. The Deitels' publications have earned international recognition, with more than 100 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 academic, corporate, government and military clients.
"Strikes a good balance between teaching computer science
fundamentals and putting data science techniques into practice.
Designed to help students not only learn programming fundamentals
but also leverage the large number of existing libraries to start
accomplishing tasks with minimal code. Concepts are accompanied by
rich Python examples that students 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
"Fun, engaging real-world examples and exercises will encourage
students to conduct meaningful data analyses. This book 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
"Great introduction to Python! This book has my strongest
recommendation both as an introduction to Python as well as Data
Science. A great introduction to IBM Watson and the services it
provides!" —Shyamal Mitra, Senior Lecturer, University of Texas
"The best designed Intro to Data Science/Python book I have seen."
—Roland DePratti, Central Connecticut State University
"You’ll develop applications using industry standard libraries and
cloud computing services." —Daniel Chen, Data Scientist, Lander
Analytics
"The book’s applied approach should engage students. The examples
involving the top-down, stepwise refinement of programs illustrate
how programs are really developed. A fantastic job providing
background on various machine learning concepts without burdening
the users with too many mathematical details." —Garrett Dancik,
Associate Professor of Computer Science/Bioinformatics, Eastern
Connecticut State University
"Wonderful for first-time Python learners from all educational
backgrounds and majors. My business analytics students had little
to no coding experience when they began the course. In addition to
loving the material, it was easy for them to follow along with the
example exercises and by the end of the course were able to mine
and analyze Twitter data using techniques learned from the book.
The chapters are clearly written with detailed explanations of the
example code, which makes it easy for students without a computer
science background to understand. The modular structure, wide range
of contemporary data science topics, and companion Jupyter
notebooks make this a fantastic resource for instructors and
students of a variety of Data Science, Business Analytics, and
Computer Science courses. The “Self Checks” are great for students.
Fabulous Big Data chapter–it covers all of the relevant programs
and platforms. Great Watson chapter! This is the type of material
that I look for as someone who teaches Business Analytics. The
chapter provided a great overview of the Watson applications. Also,
your translation examples are great for students because they
provide an “instant reward”–it’s very satisfying for students to
implement a task and receive results so quickly. Machine Learning
is a huge topic and this chapter serves as a great introduction. I
loved the housing data example–very relevant for business analytics
students. The chapter was visually stunning." –Alison Sanchez,
Assistant Professor in Economics, University of San Diego
"I like the new combination of topics from computer science, data
science, and stats. A compelling feature is the integration of
content that is typically considered in separate courses. 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
"The end-of-the-chapter problems are a real strength of this book
(and of Deitel & Deitel books in general). I would likely use this
book. The most compelling feature is that it could, theoretically,
be used for both computer science and data science programs." –Dr.
Mark Pauley, University of Nebraska at Omaha
"I agree with the authors that CS curricula should include data
science–the authors do an excellent job of combining programming
and data science topics into an introductory text. The material is
presented in digestible sections accompanied by engaging
interactive examples. This book should appeal to both computer
science students interested in high-level Python programming topics
and data science applications, and to data science students who
have little or no prior programming experience. Nearly all concepts
are accompanied by a worked-out example. A comprehensive overview
of object-oriented programming in Python–the use of graphics is
sure to engage the reader. A great introduction to Big Data
concepts, notably Hadoop, Spark, and IoT. The examples are
extremely realistic and practical." –Garrett Dancik, Eastern
Connecticut State University
"I can see students feeling really excited about playing with the
animations. Covers some of the most modern Python syntax approaches
and introduces community standards for style and documentation. The
breadth of each chapter and modular design of this book ensure that
instructors can select sections tailored to a variety of
programming skill levels and domain knowledge. The sorting
visualization program is neat. 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." –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! Very clear and engaging coverage
of recursion, searching, sorting, and especially Big O–several
“Aha” moments. The sorting animation is illustrative, useful, and
fun. I look forward to seeing the textbook in use by instructors,
students, and aspiring data scientists very soon." –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
"IBM Watson is an exciting chapter. I enjoyed running the code and
using the Watson service. The code examples put together a lot of
Watson services in a really nifty example. 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 are just great." —Daniel Chen, Data Scientist,
Lander Analytics
"A lucid exposition of the fundamentals of Python and Data Science.
Excellent section on problem decomposition. 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. Great exercises."
—Shyamal Mitra, Senior Lecturer, University of Texas
"My game programming students would appreciate these exercises."
—Pranshu Gupta, Assistant Professor, DeSales U.
"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 text! The right level for IT students. The examples are
definitely a high point to this text. I love the quantity and
quality of exercises. Avoiding heavy mathematics fits an IT program
well." —Dr. Irene Bruno, George Mason University
"A great introduction to deep learning." —Alison Sanchez,
University of San Diego
"I was very excited to see this textbook. 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
"The collection of exercises is simply amazing. I’ve learned a lot
in this review process, discovering the exciting field of AI. I
liked the Deep Learning chapter, which left me amazed with the
things that have already been achieved in this field. Many of the
projects are really interesting." —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 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
"A good overview of various neural networks with coding examples
for classification problems for which neural networks are commonly
used. The exercises in this chapter will give students insight into
how changing the structure of neural networks and the amount of
training/testing data affect performance. The Twitter examples
covering trending topics, creating word clouds, and mapping the
location of users are instructive and engaging. I like the
real-world examples of data munging. Reviewing this book was
enjoyable and even though I was fairly familiar with Python, I
ended up learning a lot." —Garrett Dancik, Associate Professor of
Computer Science/Bioinformatics, Eastern Connecticut State
University
"I really liked the live 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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