Published by Pearson (September 1, 2021) © 2022

Paul Deitel
    VitalSource eTextbook (Lifetime access)
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    ISBN-13: 9781292364933

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

    Language: English

    A ground-breaking, 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: 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 PandasDataFrames

    PART 3

    • CS: Python High-End Topics
    • CS 10. Object-Oriented Programming
    • DS Intro: Time Series and Simple Linear Regression
    • DS Intro: Time Series and Simple Linear Regression
    • 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