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Intro to Python for Computer Science and Data Science: Learning to Program with Ai, Big Data and the Cloud

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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- 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.


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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- 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.

36 review for Intro to Python for Computer Science and Data Science: Learning to Program with Ai, Big Data and the Cloud

  1. 4 out of 5

    Jorge DeFlon

    Un masivo libro de la serie Deitel que intenta ser el libro de texto universitario para las carreras de informática. Bien explicado y con buenos ejemplos y ejercicios, como toda la serie Deitel. Incluye un repaso de temas de bigdata y machine learning. Algunos temas fundamentales los trata un poco superficialmente, como por ejemplo los generadores, el TDD y la internacionalización. Debo confesar que más de la mitad del enorme volumen lo leí detalladamente, y la otra parte la leí superficialmente, pu Un masivo libro de la serie Deitel que intenta ser el libro de texto universitario para las carreras de informática. Bien explicado y con buenos ejemplos y ejercicios, como toda la serie Deitel. Incluye un repaso de temas de bigdata y machine learning. Algunos temas fundamentales los trata un poco superficialmente, como por ejemplo los generadores, el TDD y la internacionalización. Debo confesar que más de la mitad del enorme volumen lo leí detalladamente, y la otra parte la leí superficialmente, pues el tratamiento a de esos temas a profundidad amerita leer uno de los buenos libros dedicados totalmente al mismo.

  2. 4 out of 5

    Cerosh Jacob

  3. 4 out of 5

    Johan Decorte

  4. 5 out of 5

    Jorge Mejia Veron

  5. 5 out of 5

    DeAndre Yedlin

  6. 4 out of 5

    Evi

  7. 4 out of 5

    Amit

  8. 5 out of 5

    Dmytro Lakhman

  9. 4 out of 5

    Umar Arfat

  10. 4 out of 5

    Alberto Mata

  11. 4 out of 5

    Chinna

  12. 4 out of 5

    Luis JA

  13. 4 out of 5

    Mohammed Badawy

  14. 5 out of 5

    Mark

  15. 4 out of 5

    Taha

  16. 5 out of 5

    SPAS

  17. 5 out of 5

    Athar Ali

  18. 4 out of 5

    Yndy Aglr

  19. 5 out of 5

    Subba Raju

  20. 5 out of 5

    Farah Massuh

  21. 5 out of 5

    Ali Ghazi

  22. 5 out of 5

    K K

  23. 4 out of 5

    Samuel Mauricio Laime

  24. 4 out of 5

    Deivid Pos Menda

  25. 5 out of 5

    Toyoo

  26. 5 out of 5

    Jean De

  27. 5 out of 5

    MKaradeniz

  28. 5 out of 5

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  29. 4 out of 5

    Olivia

  30. 4 out of 5

    Emmy Adexon

  31. 5 out of 5

    Jagadeesh

  32. 5 out of 5

    Hassaan Malik

  33. 4 out of 5

    Giampaolo Flace

  34. 5 out of 5

    Magnus

  35. 4 out of 5

    Jaime Cabrera

  36. 4 out of 5

    Brijesh Soni

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