- Students implement hands-on, real-world case studies through free open source Python and data science libraries, free and open real-world datasets from government, industry and academia, and free, freemium and free-trial offerings of software and cloud vendors.
- Students work with artificial-intelligence technologies including natural language processing, data mining Twitter®, IBM® Watson™, speech synthesis, speech recognition, supervised and unsupervised machine learning, deep learning, and big data with Hadoop, Spark, SQL/NoSQL and the Internet of Things (IoT).
- Extensive static, dynamic and interactive 2D and 3D visualisations and animations.
- Artificial Intelligence–a key intersection between computer science and data science–is emphasised, with all six data-science implementation case study chapters rooted in AI technologies and/or discussions of the big data hardware and software infrastructure that enables AI-based solutions.
- Content is divided into groups of related chapters that instructors can easily include or omit.
- The Preface includes a chapter dependency chart to help instructors plan their syllabi.
- Chapters 1–11 cover the examples, exercises and projects (EEPs) traditionally associated with introductory computer-science Python programming courses.
- Chapters 1–10 each include optional brief Intro to Data Science sections that prepare students for the Data Science Case Studies in Chapters 12–17. In these intro sections, the Deitels present data science history and terminology, Python's statistics module, basic descriptive statistics, measures of central tendency, measures of dispersion, static and dynamic visualisations (Seaborn and Matplotlib), simulation, data preparation with pandas, CSV file manipulation, time series and simple linear regression.
- Chapters 12–17 are fully implemented AI- and big-data-based data-science case studies.
- Most instructors will cover the core Python content. Computer-science courses will likely work through more of Chapters 1–11 and fewer of Chapters 12–17 and the Intro to Data Science sections. Data science courses will likely work through the Intro to Data Science sections, fewer of Chapters 1–11 and more of Chapters 12–17.
- Functional-Style Programming Topics help students write more concise programs that are easier to debug and parallelise.
- Examples, exercises, projects (EEPs) and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming, while also involving them in hands-on data science.
- Jupyter Notebooks allow users to combine text, graphics, audio, video and interactive coding functionality, in a web browser for interactive programming exercises and self-checks.
- Self-Check Exercises and Answers after most sections enable students to test their knowledge of the concepts with short-answer questions and interactive IPython coding sessions.
- Copyright 2020
- Dimensions: 7" x 9-1/8"
- Pages: 880
- Edition: 1st
- ISBN-10: 0-13-540467-3
- ISBN-13: 978-0-13-540467-6
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
- 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