This chapter gives you an overview of the tools available for data analysis in Python, with details concerning the Python packages and libraries that will be used in this book. A few installation tips are given, and the chapter concludes with a brief example. We will concentrate on how to read data files, select data, and produce simple plots, instead of delving into numerical data analysis.
Mastering Python Data Analysis
By :
Mastering Python Data Analysis
By:
Overview of this book
Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want!
Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You’ll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making.
You’ll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you’ll move on to review statistical inference using Python, Pandas, and SciPy. After that, we’ll focus on performing regression using computational tools and you’ll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you’ll discover how to use Python’s tools for supervised machine learning.
Table of Contents (15 chapters)
Mastering Python Data Analysis
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
Tools of the Trade
Exploring Data
Learning About Models
Regression
Clustering
Bayesian Methods
Supervised and Unsupervised Learning
Time Series Analysis
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