TY  - BOOK
AU  - Deitel, Paul
AU  - Deitel, Harvey
TI  - Intro to Python for computer science and data science
SN  - 9781292364902
PY  - 2022///
PB  - Pearson
KW  - QA MATHEMATICS
KW  - Python (Computer program language)
KW  - Computer science
KW  - Data Science
KW  - Cognitive Computing
KW  - Artificial Intelligence
KW  - Big Data
KW  - Cloud
KW  - Machine Learning
N1  - learning to program with AI, big data and the cloud; 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 PandasDataFrames -- 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 inthe Exercises DS 13. Data Mining Twitter (R): Sentiment Analysis, JSON andWeb Services DS 14. IBM Watson (R) and Cognitive Computing DS 15. Machine Learning: Classification, Regression and Clustering DS 16. Deep Learning Convolutional and Recurrent NeuralNetworks; Reinforcement Learning in the Exercises DS 17. Big Data: Hadoop (R), Spark (TM), NoSQL and IoT
N2  - 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
ER  -