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Python is one of the most popular languages today. Relatively new
fields such as data science, AI, robotics, and data analytics, along with
traditional professions such as web development and scientific research,
are embracing Python. It’s increasingly important for programmers writing
code in a dynamic language like Python to make sure that the code is high-quality
and error-free. As a Python developer, you want to make sure that
the software you are building makes your users happy without going over
budget or never releasing.
Python is a simple language, yet it’s difficult to write great code because
there aren’t many resources that teach how to write better Python code.
Currently lacking in the Python world are code consistency, patterns,
and an understanding of good Pythonic code among developers. For every
Python programmer, great Pythonic code has a different meaning. The
reason for this could be that Python is being used in so many areas that it’s
difficult to reach consensus among developers about specific patterns. In
addition, Python doesn’t have any books about clean code like Java and
Ruby do. There have been attempts to write those kinds of books to bring
clarity to good Python practices, but those attempts have been few and far
between, and quickly frankly, they haven’t been high-quality.
The main goal of this book is to provide tips to Python developers of
various levels so they can write better Python software and programs. This
book gives you various techniques irrespective of the field you use Python
in. This book covers all levels of Python, from basic to advanced, and
shows you how to make your code more Pythonic.
Remember, writing software is not only science but art, and this book
will teach you how to become a better Python programmer.
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 Applying Predictive Analytics: Finding Value in Data
This textbook presents a practical approach to predictive analytics for classroom learning. It focuses on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be... |  |  Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More
Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media—including who’s connecting with whom, what they’re... |  |  |
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