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

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

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Multitiered Learning Approach

E-Book at VitalSource.com or Redshelf.com

Students may rent or purchase e-books from

VideoNotes

  • We provide extensive VideoNotes in which co-author Paul Deitel teaches the core Python concepts in Chapters 1-10.
  • These videos, plus additional videos based on Chapters 12-17 are available to Safari Online Learning subscribers in our Python Fundamentals LiveLessons video training
  • The videos also can be purchased in five parts from https://informit.com
    • Parts 1-3 correspond to Chapters 1-10, so anyone with access to the VideoNotes does not need to purchase these parts.

Anaconda Python Distribution

  • Windows, macOS and Linux
  • Python, the IPython interpreter and Jupyter Notebooks
  • Includes wide range of software packages and libraries commonly used in Python programming, scientific computing, data science, AI and more

Links to Each Chapter's Objectives/Outline Slides

The notebooks linked here each have links to the corresponding chapter's slides:

  1. Introduction to Computers and Python
  2. Introduction to Python Programming
  3. Control Statements and Program Development
  4. Functions
  5. Sequences: Lists and Tuples
  6. Dictionaries and Sets
  7. Array-Oriented Programming with NumPy
  8. Strings: A Deeper Look
  9. Files and Exceptions
  10. Object-Oriented Programming
  11. Computer Science Thinking: Recursion, Searching, Sorting and Big O
  12. Natural Language Processing (NLP)
  13. Data Mining Twitter
  14. IBM Watson and Congnitive Computing
  15. Machine Learning: Classification, Regression and Clustering
  16. Deep Learning
  17. Big Data: Hadoop, Spark, NoSQL and IoT

©1992–2020 by Pearson Education, Inc. All Rights Reserved. This content is based on Chapter 1 of the book Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud.

DISCLAIMER: The authors and publisher of this book have used their best efforts in preparing the book. These efforts include the development, research, and testing of the theories and programs to determine their effectiveness. The authors and publisher make no warranty of any kind, expressed or implied, with regard to these programs or to the documentation contained in these books. The authors and publisher shall not be liable in any event for incidental or consequential damages in connection with, or arising out of, the furnishing, performance, or use of these programs.