We have now identified clusters using a range of methods, from calculating simple centroids manually to advanced hierarchical clustering algorithms in SciPy. There are of course many more packages in Python. We will look at one alternative, the machine learning package, Scikit-learn, in Chapter 7 , Supervised and Unsupervised Learning, to identify clusters. SciPy has these two clustering frameworks, that is, vector quantization and hierarchical clustering, which lay the foundation for cluster analysis and are very useful in many general data analysis problems. In the next chapter, we will look at Bayesian analysis and how to use the PyMC Bayesian inference package in Python to characterize various things in data.
Mastering Python Data Analysis
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Mastering Python Data Analysis
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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.
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Mastering Python Data Analysis
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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
More on Jupyter Notebook and matplotlib Styles
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