Python for Data Science: Comprehensive Guide of Tips and Tricks using Python Data Science Theories

Kirjan Python for Data Science: Comprehensive Guide of Tips and Tricks using Python Data Science Theories (2020) on kirjoittanut Ethan Williams. Kirjan kieli on englanti, ja sen on kustantanut Independently Published. Kirja on laajuudeltaan 202 sivua ja se on nidottu.

Python for Data Science is a comprehensive guide about how to perform data science with Python. This book is for students, researchers, and developers who are technically-minded, and have a wide background in writing code as well as using numerical and computational tools. However, many of you may don't wish to learn Python, but instead wish to learn the language in hopes of utilizing it as a means for computational and data-intensive science. The aim of this book is not meant to serve as a kind of introduction to Python or even programming in general; we presume readers will get their hands on this book already possess ample amount of knowledge in the Python language, which includes assigning variables, defining functions, controlling a program's flow, calling methods of objects, and other basic operations. Rather, the book was put together to assist users of Python to understand how to use the data science stack of Python - with libraries including NumPy, pandas, Matplotlib and other such tools - with the aim of effectively manipulating, storing, and getting data insight. In this book, we'll cover a variety of topics, including several libraries, such as NumPy that offers the ndarray for efficient manipulation and storage of dense data arrays in Python. Then you'll be able to learn how to manipulate data using Pandas, a library that offers the DataFrame object for efficient manipulation and storage of columnar/label data in Python. After that, we'll talk about the Matplotlib library and how it provides a range of flexible data visualization capabilities in Python. Then we'll talk the types of machine learning involved including supervised and unsupervised learning. Lastly, we'll finish with multiple regression analysis, which is a combination of techniques that help us study the straight-line relations between two or more variables. In addition, this book is backed by a series of examples and case studies to help clarify anything that is a little too hard for you to absorb. We are confident that you will make fine a data scientists going forward