(eBook PDF) Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud – Digital Ebook – Instant Delivery Download

Product details:

• ISBN-10 ‏ : ‎ 0135404673
• ISBN-13 ‏ : ‎ 978-0135404676
• Author: Paul J. Deitel

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 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

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