9780137508518

Summary

Introduction to Python for Computer Science and Data Science takes a unique, modular approach to teaching and learning introductory Python programming that is relevant for both computer science and data science audiences. The Deitels cover the most current topics and applications to prepare you for your career. Jupyter Notebooks supplements provide opportunities to test your programming skills. Fully implemented case studies in artificial intelligence technologies and big data let you apply your knowledge to interesting projects in the business, industry, government and academia sectors. Hundreds of hands-on examples, exercises and projects offer a challenging and entertaining introduction to Python and data science.

Author Biography

About our authors

Paul J. Deitel, CEO and Chief Technical Officer of Deitel & Associates, Inc., is an MIT graduate with 38 years of computing and corporate training experience and is an Oracle^® Java^® Champion and a Microsoft^® C# MVP (2012-2014). He is a best-selling programming-language textbook/professional book/video/e-learning author. Paul is one of the world's most experienced programming-languages trainers. Through Deitel & Associates, Inc., he has delivered hundreds of programming courses worldwide to clients, including 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, 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 M. Deitel, Chairman and Chief Strategy Officer of Deitel & Associates, Inc., has over 55 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 just before they spun off Computer Science programs. 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 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.

Download a PDF to view the unique Table of Contents.

PART 1

CS: Python Fundamentals Quickstart
CS 1. Introduction to Computers and Python
DS Intro: AI - at the Intersection of CS and DS
CS 2. Introduction to Python Programming
DS Intro: Basic Descriptive Stats
CS 3. Control Statements and Program Development
DS Intro: Measures of Central Tendency - Mean, Median, Mode
CS 4. Functions
DS Intro: Basic Statistics - Measures of Dispersion
CS 5. Lists and Tuples
DS Intro: Simulation and Static Visualization

PART 2

CS: Python Data Structures, Strings and Files
CS 6. Dictionaries and Sets
DS Intro: Simulation and Dynamic Visualization
CS 7. Array-Oriented Programming with NumPy, High-Performance NumPy Arrays
DS Intro: Pandas Series and DataFrames
CS 8. Strings: A Deeper Look Includes Regular Expressions
DS Intro: Pandas, Regular Expressions and Data Wrangling
CS 9. Files and Exceptions
DS Intro: Loading Datasets from CSV Files into Pandas DataFrames

PART 3

CS: Python High-End Topics
CS 10. Object-Oriented Programming
DS Intro: Time Series and Simple Linear Regression
CS 11. Computer Science Thinking: Recursion, Searching, Sorting and Big O
CS and DS Other Topics Blog

PART 4

AI, Big Data and Cloud Case Studies
DS 12. Natural Language Processing (NLP), Web Scraping in the Exercises
DS 13. Data Mining Twitter^®: Sentiment Analysis, JSON and Web Services
DS 14. IBM Watson^® and Cognitive Computing
DS 15. Machine Learning: Classification, Regression and Clustering
DS 16. Deep Learning Convolutional and Recurrent Neural Networks; Reinforcement Learning in the Exercises
DS 17. Big Data: Hadoop^®, Spark^™, NoSQL and IoT

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