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