By Samir Madhavan
Approximately This Book
Master information technological know-how equipment utilizing Python and its libraries
Create information visualizations and mine for patterns
Advanced strategies for the 4 basics of knowledge technology with Python - info mining, info research, info visualization, and computer learning
Who This booklet Is For
If you're a Python developer who desires to grasp the realm of knowledge technological know-how then this ebook is for you. a few wisdom of information technological know-how is assumed.
What you are going to Learn
Manage information and practice linear algebra in Python
Derive inferences from the research by way of appearing inferential statistics
Solve info technological know-how difficulties in Python
Create high-end visualizations utilizing Python
Evaluate and follow the linear regression strategy to estimate the relationships between variables.
Build advice engines with many of the collaborative filtering algorithms
Apply the ensemble how you can increase your predictions
Work with sizeable information applied sciences to address info at scale
Data technology is a comparatively new wisdom area that's utilized by a variety of enterprises to make facts pushed judgements. info scientists need to put on numerous hats to paintings with info and to derive worth from it. The Python programming language, past having conquered the clinical neighborhood within the final decade, is now an essential software for the knowledge technology practitioner and a must-know instrument for each aspiring information scientist. utilizing Python will provide you with a quick, trustworthy, cross-platform, and mature setting for information research, computer studying, and algorithmic challenge solving.
This accomplished advisor is helping you progress past the hype and go beyond the speculation by means of supplying you with a hands-on, complicated learn of information science.
Beginning with the necessities of Python in info technology, you are going to learn how to deal with info and practice linear algebra in Python. you'll circulation directly to deriving inferences from the research via acting inferential information, and mining facts to bare hidden styles and tendencies. you'll use the matplot library to create high-end visualizations in Python and discover the basics of laptop studying. subsequent, you'll follow the linear regression strategy and in addition learn how to follow the logistic regression strategy to your functions, sooner than developing advice engines with quite a few collaborative filtering algorithms and enhancing your predictions through employing the ensemble methods.
Finally, you are going to practice K-means clustering, in addition to an research of unstructured facts with various textual content mining strategies and leveraging the ability of Python in colossal information analytics.
Style and approach
This ebook is an easy-to-follow, complete consultant on info technology utilizing Python. the subjects coated within the booklet can all be utilized in actual global scenarios.
Read Online or Download Mastering Python for Data Science PDF
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Extra info for Mastering Python for Data Science
As for the first two editions, I will also be maintaining a log on this web site of Python changes over time, which you should consider a supplemental appendix to this text. com, also has an errata report system, and you should consider the union of these two lists to be the official word on book bugs and updates. xl | Preface Contacting O’Reilly You can also address comments and questions about this book to the publisher: O’Reilly Media, Inc. 1005 Gravenstein Highway North Sebastopol, CA 95472 800-998-9938 (in the United States and Canada) 707-827-7000 (international/local) 707-829-0104 (fax) O’Reilly has a web page for this book, which lists errata, examples, and any additional information.
When time is tight, it’s more convenient to run scripts on the same platform as your publishing tools than to frequently switch * I regret cutting this chapter, but new material was added, and as you can tell, this is already a substantial book. As my first editor, Frank Willison, famously said when the second edition came out, if this book were run over by a truck, it would do damage…. Preface | xxix platforms. Luckily, because Python has become so portable, the underlying operating system is largely irrelevant to developers.
PP3E\System\Streams>, it is really to be typed at a system command-line prompt, while working in the PP3E\System\Streams subdirectory in your examples directory. Unix and Linux users: think / when you see \ in filename paths. Preface | xxxiii Example launchers Because it’s just plain fun to click on things right away, there are also selfconfiguring demo launcher programs (described later), to give you a quick look at Python scripts in action with minimal configuration requirements. You can generally run them straight from the examples package without any configuration.