Features
Hallmark features of this title
Current real-world applications
- Hundreds of examples, exercises and projects (EEPs) offer a hands-on introduction to Python and data science.
- AI, big data and the Cloud are explored in 6 fully implemented data science case studies.
- Jupyter Notebooks supplements give students practice working in a live coding environment.
- Self-Check exercises with answers let students test their knowledge using short-answer questions and interactive iPython coding sessions.
Unique modular organization of computer and data science topics
- Content is divided into groups of related chapters. Python content and optional intros to data science are presented early. Later chapters dive deeper into data science.
- A chapter dependency chart helps instructors easily plan their syllabi.
- Copyright 2020
- Pages: 880
- Edition: 1st
-
eBook (Adobe DRM)
- ISBN-10: 0-13-540476-2
- ISBN-13: 978-0-13-540476-8
This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book.
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.
Table of Contents
Brief Contents
To see a visual view of the unique Table of Contents, download the PDF.
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