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Intro to Python for Computer Science and Data Science : Learning to Program with AI, Big Data and the Cloud by Harvey Deitel and Paul Deitel (2019, Trade Paperback)
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About this product
Product Identifiers
PublisherPearson Education
ISBN-100135404673
ISBN-139780135404676
eBay Product ID (ePID)28038257079
Product Key Features
Number of Pages880 Pages
Publication NameIntro to Python for Computer Science and Data Science : Learning to Program with AI, Big Data and the Cloud
LanguageEnglish
SubjectProgramming Languages / Basic, Programming Languages / Python
Publication Year2019
TypeTextbook
AuthorHarvey Deitel, Paul Deitel
Subject AreaComputers
FormatTrade Paperback
Dimensions
Item Height1.4 in
Item Weight43 Oz
Item Length9.1 in
Item Width7 in
Additional Product Features
Intended AudienceCollege Audience
LCCN2019-003447
Dewey Edition23
Dewey Decimal005.13/3
Table Of ContentPART 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®, SparkTM, NoSQL and IoT
SynopsisFor 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., Introduction to Python for Computer Science and Data Science takes a unique, modular approach to teaching and learning introductory Python programming that is relevant for both computer science and data science audiences. The Deitels cover the most current topics and applications to prepare you for your career. Jupyter Notebooks supplements provide opportunities to test your programming skills. Fully implemented case studies in artificial intelligence technologies and big data let you apply your knowledge to interesting projects in the business, industry, government and academia sectors. Hundreds of hands-on examples, exercises and projects offer a challenging and entertaining introduction to Python and data science., 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.
LC Classification NumberQA76.73.P98D45 2020
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