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

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Introduction to Python for Computer Science and Data Science is your guide to introductory Python programming. This engaging text provides real-world datasets and artificial-intelligence technologies to prepare you for meaningful projects. Examples, exercises, and implementation case studies are included.

Published by Pearson (July 14th 2021) - Copyright © 2020

ISBN-13: 9780137505494

Subject: Pre-Programming & Introduction to Programming

Category: Python

Table of contents

To see a visual view of the unique Table of Contents, download the PDF.


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


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


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


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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