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Python for Data Science For Dummies (For Dummies (Computer/Tech))

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The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library.  Python For Data Science For Dummies is written The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library.  Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud. Get started with data science and Python Visualize information Wrangle data Learn from data The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.


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The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library.  Python For Data Science For Dummies is written The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library.  Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud. Get started with data science and Python Visualize information Wrangle data Learn from data The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.

35 review for Python for Data Science For Dummies (For Dummies (Computer/Tech))

  1. 5 out of 5

    Ru Sun

    This book is a good introduction to Python and data science that covers a broad range of topics. I give it four stars for content. In terms of writing, formatting, and overall quality, I would give it two stars at most. The book seems to be published in a hurry without any editing. 1) There are at least a dozen errors in the book. For example, at one place it says that values above upper quartile and below lower quartile are outliers. This is totally wrong since each accounts for 25% of the data! This book is a good introduction to Python and data science that covers a broad range of topics. I give it four stars for content. In terms of writing, formatting, and overall quality, I would give it two stars at most. The book seems to be published in a hurry without any editing. 1) There are at least a dozen errors in the book. For example, at one place it says that values above upper quartile and below lower quartile are outliers. This is totally wrong since each accounts for 25% of the data! Later chapters do provide the correct information. Several similar incidents give the distinct impression that the book is written by two authors. 2) Many terms are used inconsistently or even incorrectly throughout the book, including samples, examples, variables, predictors, classes, models, algorithms, etc. Again, it is likely due to the different authorship and lack of editing. 3) Introduction of datasets and concepts is out of order. For example, a dataset is used in a task, then a few chapters later, the same dataset appears again with a lengthy explanation of its background and details. Same with concepts - direct use first, definition later. 4) Sloppy writing. Lots of sentences are simply awkward. For example, "Each tree tries to build a model that successfully predicts what trees that were built before it weren't able to forecast". Repetitive words and phrases abound - A paragraph with five lines might include the word 'example' four times; "In fact" appears at least 100 times in the book. I won't list all the formatting problems in case people think I am OCD. To summarize, this book gives a good overview of Python and Data Science to get people started in the field. A thorough careful editing would make it more valuable and less annoying.

  2. 4 out of 5

    Ben Rogers

    This was a good coding book. I enjoyed following along with the code and writing my own examples. Great book to learn different libraries for data science manipulations in Python. Would recommend! 3.9/5

  3. 4 out of 5

    Dima Kaptsan

    A bit weak on python, the reader should know the basics of python A good overview of ml models

  4. 5 out of 5

    Ryan Rubidoux Cosman

  5. 5 out of 5

    ChukwuNonso Asika

  6. 5 out of 5

    Lavanya

  7. 4 out of 5

    Shivaprasad Mani tripathi

  8. 4 out of 5

    Victoria

  9. 5 out of 5

    UndeadArtist

  10. 5 out of 5

    ?

  11. 4 out of 5

    Raghav

  12. 4 out of 5

    Joseph

  13. 5 out of 5

    Tan Karen

  14. 4 out of 5

    Roud

  15. 5 out of 5

    Mehul Sheth

  16. 4 out of 5

    Jamie

  17. 5 out of 5

    Sierra

  18. 4 out of 5

    Jeremy

  19. 5 out of 5

    Kadiri Saliu

  20. 4 out of 5

    Sharday Brown

  21. 5 out of 5

    Chukson

  22. 4 out of 5

    Adibah Nur

  23. 5 out of 5

    Manuel Gonzalez

  24. 5 out of 5

    Claudia

  25. 5 out of 5

    Fabricio Echeverría

  26. 4 out of 5

    Michael Murphy

  27. 4 out of 5

    Jesse

  28. 5 out of 5

    Milcolumbus Mowlington

  29. 4 out of 5

    Daniel Laby

  30. 4 out of 5

    Pedro Dullius

  31. 4 out of 5

    Vica

  32. 4 out of 5

    Ana Venâncio

  33. 4 out of 5

    Manny

  34. 5 out of 5

    Saye

  35. 5 out of 5

    Migdalia Gutierrez

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