Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud, 1st edition

  • Paul Deitel
  • Harvey M. Deitel

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

ISBN-13:  9780135404676

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$69.32 $86.65

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  • Paperback

    You'll get a bound printed text.


 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

  • 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

Published by Pearson (February 15th 2019) - Copyright © 2020