Python for Programmers

"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 "I

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.

Part 1-Python Fundamentals 1 Introduction to Computers and Python 2 Introduction to Python Programming 3 Control Statements; Program Development 4 Functions 5 Lists and Tuples Part 2-Python Data Structures, Files and Databases 6 Arrays 7 Sets and Dictionaries 8 Strings: A Deeper Look 9 File and Exceptions 10 SQL Databases Part 3-Python High-End Topics 11 Object-Based Programming: Classes and Objects 12 Object-Oriented Programming: Inheritance and Polymorphism 13 tkinter GUI 14 turtle Graphics and tkinter-Based Canvas Graphics 15 Concurrency and Parallelism 16 Game Programming with PyGame 17 Python Other Topics Part 4-Python-Based Data-Science Case Studies 18 Natural Language Processing (NLP) 19 Data Mining Twitter: Web Services and JSON 20 Supervised Machine Learning 21 Unsupervised Machine Learning 22 Deep Learning 23 Reinforcement Learning 24 NoSQL and NewSQL Databases 25 Big Data with Hadoop 26 Big Data with Spark; Internet of Things (IoT) 27 Special Feature: IBM Watson Analytics and Cognitive Computing