Book Name: Data Science from Scratch: First Principles with Python Second Edition
Author: Joel Grus
Publisher: O’Reilly Media
ISBN-10, 13: 9781492041139,1492041130
Pages: 406 pages
File size: 4 MB
File format: PDF,EPUB
Data Science from Scratch: First Principles with Python Second Edition Pdf Book Description:
Data science is very popular nowday and for you who want to know more about it you have to read and download this book that we give it to you for free. you have learn the Data Science from Scratch first edition and now you have to read this second edition. we sure that the book will help you to done your job because this book give you the great explanation that you have to know when you are doing data science with python. Data scientist has been called “the sexiest job of the 21st century,” presumably by someone who has never visited a fire station.Nonetheless, data science is a hot and growing field, and it doesn’t take a great deal of sleuthing to find analysts breathlessly prognosticating that over the next 10 years, we’ll need billions and billions more data scientists than we currently have. But what is data science? After all, we can’t produce data scientists if we don’t know what data science is. According to a Venn diagram that is somewhat famous in the industry.
Although I originally intended to write a book covering all three, I quickly realized that a thorough treatment of “substantive expertise” would require tens of thousands of pages. At that point, I decided to focus on the first two. My goal is to help you develop the hacking skills that you’ll need to get started doing data science. And my goal is to help you get comfortable with the mathematics and statistics that are at the core of data science. This is a somewhat heavy aspiration for a book. The best way to learn hacking skills is by hacking on things. By reading this book, you will get a good understanding of the way I hack on things, which may not necessarily be the best way for you to hack on things. You will get a good understanding of some of the tools I use, which will not necessarily be the best tools for you to use. You will get a good understanding of the way I approach data problems, which may not necessarily be the best way for you to approach data problems. The intent (and the hope) is that my examples will inspire you to try things your own way. All the code and data from the book is available on GitHub to get you started. There are lots and lots of data science libraries, frameworks, modules, and toolkits that efficiently implement the most common (as well as the least common) data science algorithms and techniques. If you become a data scientist, you will become intimately familiar with NumPy, with scikit learn, with pandas, and with a panoply of other libraries. They are great for doing data science. But they are also a good way to start doing data science without actually understanding data science.
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