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

About

Features

Hallmark features of this title

Current real-world applications

• Hundreds of examples, exercises and projects (EEPs) offer a hands-on introduction to Python and data science.
• AI, big data and the Cloud are explored in 6 fully implemented data science case studies.
• Jupyter Notebooks supplements give students practice working in a live coding environment.
• Self-Check exercises with answers let students test their knowledge using short-answer questions and interactive iPython coding sessions.

Unique modular organization of computer and data science topics

• Content is divided into groups of related chapters. Python content and optional intros to data science are presented early. Later chapters dive deeper into data science.
• A chapter dependency chart helps instructors easily plan their syllabi.

Description

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book.

 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

Brief 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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