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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.

44 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

    Pascal

    If you are buying this book because you need it in a college class setting, where you have a professor who can explain to you how to solve the 20 to 30 examples after each chapter, you can breath out now, cause you will probably be fine with this book. If you think you can go through this book by yourself, I have bad news for you....not a chance.... :( the chapters provide you with good information but only about 20 percent of the information, which you actually need to solve the example problem If you are buying this book because you need it in a college class setting, where you have a professor who can explain to you how to solve the 20 to 30 examples after each chapter, you can breath out now, cause you will probably be fine with this book. If you think you can go through this book by yourself, I have bad news for you....not a chance.... :( the chapters provide you with good information but only about 20 percent of the information, which you actually need to solve the example problems at the end of the chapters. This will leave you stranded and hanging and you will chase the internet for 3rd party help spending hours trying to solve the examples instead of learning on how to solve them from the book. Sorry I really wanted to like this book but its not for the Individual who wants to teach him/herself but only for students in a classroom setting in conjunction with teaching staff.

  2. 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.

  3. 4 out of 5

    Dan Contreras

    Una excelente introducción a python que lei de portada a portada y completé todos los ejercicios. El libro se pone medio seco en los capítulos intermedios que tienen que ver con listas, arreglos, texto y demás temas de programación abstracta, pero eso es por que nunca me han gustado esos temas. Los capítulos 15 y 16, de Machine Learning y Redes Neuronales, son una joyita y valen por si solos el precio de admisión. Altamente recomendado como primer intro a Python.

  4. 5 out of 5

    Seth Dickerson

    Learned a lot of interesting python codes and its uses which I believe are well explained to teach the fundsmentals.

  5. 4 out of 5

    Cerosh Jacob

  6. 4 out of 5

    Johan Decorte

  7. 4 out of 5

    Jorge Mejia Veron

  8. 4 out of 5

    DeAndre Yedlin

  9. 4 out of 5

    Evi

  10. 4 out of 5

    Leopoldo

  11. 4 out of 5

    Ashley

  12. 5 out of 5

    Toni Niittymäki

  13. 4 out of 5

    Amit

  14. 5 out of 5

    Dmytro Lakhman

  15. 5 out of 5

    Umar Arfat

  16. 5 out of 5

    Jovany Agathe

  17. 4 out of 5

    Robert D.

  18. 4 out of 5

    Alberto Mata

  19. 5 out of 5

    Chinna

  20. 5 out of 5

    Luis JA

  21. 5 out of 5

    Mohammed Badawy

  22. 4 out of 5

    Mark

  23. 5 out of 5

    Taha

  24. 5 out of 5

    SPAS

  25. 5 out of 5

    Athar Ali

  26. 5 out of 5

    Yndy Aglr

  27. 4 out of 5

    Subba Raju

  28. 4 out of 5

    Farah Massuh

  29. 5 out of 5

    Ali Ghazi

  30. 5 out of 5

    K K

  31. 4 out of 5

    Samuel Mauricio Laime

  32. 4 out of 5

    Deivid Pos Menda

  33. 5 out of 5

    Toyoo

  34. 4 out of 5

    Jean De

  35. 5 out of 5

    MKaradeniz

  36. 5 out of 5

    Eduardo

  37. 4 out of 5

    Olivia

  38. 5 out of 5

    Emmy Adexon

  39. 4 out of 5

    Jagadeesh

  40. 5 out of 5

    Hassaan Malik

  41. 4 out of 5

    Giampaolo Flace

  42. 4 out of 5

    Magnus

  43. 5 out of 5

    Jaime Cabrera

  44. 4 out of 5

    Brijesh Soni

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