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

by Deitel, Paul; Deitel, Harvey M.

• ISBN13: 9780135404676
• ISBN10: 0135404673
• eBook ISBN(s): 9780135404812
• Additional ISBN(s): 9781292364902
• Format: Paperback
• Copyright: 2019-02-15
• Publisher: Pearson
• More Book Details

Summary

 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.

 

Author Biography

 

Table of Contents

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PART 1

CS: Python Fundamentals Quickstart

CS 1. Introduction to Computers and Python

DS Intro: A Brief Tour of Data Science and Artificial Intelligence

CS 2. Introduction to Python Programming

DS Intro: Basic Descriptive Stats

CS 3. Control Statements; 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: High-Performance NumPy Arrays

DS Intro: Pandas Series and DataFrames

CS 8. Strings: A Deeper Look; 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. tkinter Graphical User Interfaces

CS 12. Computer Science Thinking: Recursion, Searching, Sorting and Big O

CS Other Topics Blog

 

PART 4

AI, Cloud and Big Data Case Studies

DS 13. Natural Language Processing (NLP), Web Scraping (in exercises)

DS 14. Data Mining Twitter®: Sentiment Analysis, JSON and Web Services

DS 15. IBM® Watson™ and Cognitive Computing

DS 16. Machine Learning: Classification, Regression and Clustering

DS 17. Deep Learning and Reinforcement Learning

DS 18. Big Data: Hadoop®, Spark™, NoSQL and IoT