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

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

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

PART 1

• CS: Python Fundamentals Quickstart
• CS 1. Introduction to Computers and Python
• DS Intro: AIat 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 TendencyMean, 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®, SparkTM, NoSQL and IoT

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