Intro to Python for Computer Science and Data Science Learning to Program with AI, Big Data and The Cloud

 For introductory-level Python programming and/or data-science courses.

A groundbreaking, flexible approach to computer science and data science

The Deitels’ Introduction to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud offers a unique approach to teaching introductory Python programming, appropriate for both computer-science and data-science audiences. Providing the most current coverage of topics and applications, the book is paired with extensive traditional supplements as well as Jupyter Notebooks supplements. Real-world datasets and artificial-intelligence technologies allow students to work on projects making a difference in business, industry, government and academia. Hundreds of examples, exercises, projects (EEPs), and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming and hands-on data science.

The book's modular architecture enables instructors to conveniently adapt the text to a wide range of computer-science and data-science courses offered to audiences drawn from many majors. Computer-science instructors can integrate as much or as little data-science and artificial-intelligence topics as they'd like, and data-science instructors can integrate as much or as little Python as they'd like. The book aligns with the latest ACM/IEEE CS-and-related computing curriculum initiatives and with the Data Science Undergraduate Curriculum Proposal sponsored by the National Science Foundation.

PART 1

CS: Python Fundamentals Quickstart

CS 1. Introduction to Computers and Python

DS Intro: A Brief Tour of Data Science and Artificial Intelligence

CS 2. Introduction to Python Programming

DS Intro: Basic Descriptive Stats

CS 3. Control Statements; 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: High-Performance NumPy Arrays

DS Intro: Pandas Series and DataFrames

CS 8. Strings: A Deeper Look; 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. tkinter Graphical User Interfaces

CS 12. Computer Science Thinking: Recursion, Searching, Sorting and Big O

CS Other Topics Blog

PART 4

AI, Cloud and Big Data Case Studies

DS 13. Natural Language Processing (NLP), Web Scraping (in exercises)

DS 14. Data Mining Twitter®: Sentiment Analysis, JSON and Web Services

DS 15. IBM® Watson™ and Cognitive Computing

DS 16. Machine Learning: Classification, Regression and Clustering

DS 17. Deep Learning and Reinforcement Learning

DS 18. Big Data: Hadoop®, Spark™, NoSQL and IoT

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.