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

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

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Features

Prepares students for future careers with the most current and relevant real-world applications

  • Students implement hands-on, real-world case studies through free open source Python and data science libraries, free and open real-world datasets from government, industry and academia, and free, freemium and free-trial offerings of software and cloud vendors.
  • Students work with artificial-intelligence technologies including natural language processing, data mining Twitter®, IBM® Watson™, speech synthesis, speech recognition, supervised and unsupervised machine learning, deep learning, and big data with Hadoop, Spark, SQL/NoSQL and the Internet of Things (IoT).
  • Extensivestatic, dynamic and interactive 2D and 3D visualizations and animations.
  • Artificial Intelligencea key intersection between computer science and data scienceis emphasized, with all six data-science implementation case study chapters rooted in AI technologies and/or discussions of the big data hardware and software infrastructure that enables AI-based solutions.
  • A companion website, www.pearson.com/deitel, contains dynamic support resources for instructors and students:
    • VideoNotes.
    • Live animations in source-code files and Jupyter Notebooks enable students to conveniently edit the code, modify animation parameters and re-execute the animations.
    • Many open source visualization packages have animation capabilities for dynamic visualization, and some can turn animations into videos. Students will use visualization libraries and tools like Matplotlib, Seaborn and Folium to make data come alive.

Helps instructors adapt to a range of computer-science and data-science courses with the flexible modular architecture

  • Content is divided into groups of related chapters that instructors can easily include or omit.
    • The Preface includes a chapter dependency chart to help instructors plan their syllabi.
    • Chapters 1–11 cover the examples, exercises and projects (EEPs) traditionally associated with introductory computer-science Python programming courses.
    • Chapters 1–10 each include optional brief Intro to Data Science sections that prepare students for the Data Science Case Studies in Chapters 12–17. In these intro sections, the Deitels present data science history and terminology, Python's statistics module, basic descriptive statistics, measures of central tendency, measures of dispersion, static and dynamic visualizations (Seaborn and Matplotlib), simulation, data preparation with pandas, CSV file manipulation, time series and simple linear regression.
    • Chapters 12–17 are fully implemented AI- and big-data-based data-science case studies.
  • Most instructors will cover the core Python content. Computer-science courses will likely work through more of Chapters 1–11 and fewer of Chapters 12–17 and the Intro to Data Science sections. Data science courses will likely work through the Intro to Data Science sections, fewer of Chapters 1–11 and more of Chapters 12–17.
  • Functional-Style Programming Topics help students write more concise programs that are easier to debug and parallelize.

Provides hundreds of real-world examples, challenging exercises, and projects for both computer science and data science topics

  • Examples, exercises, projects (EEPs) and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming, while also involving them in hands-on data science.
  • Jupyter Notebooks allow users to combine text, graphics, audio, video and interactive coding functionality, in a web browser for interactive programming exercises and self-checks.
  • Self-Check Exercises and Answers after most sections enable students to test their knowledge of the concepts with short-answer questions and interactive IPython coding sessions.

Check out the preface for a complete list of features.


Description

  • Copyright 2020
  • Dimensions: 7" x 9-1/8"
  • Pages: 880
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-540467-3
  • ISBN-13: 978-0-13-540467-6

 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.

Sample Content

Table of 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

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