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Preface
The use of Python for data analysis and visualization has only increased in
popularity in the
last few years.
The aim of this book is to develop skills to effectively approach almost any data
analysis problem, and extract all of the available information. This is done by
introducing a range of varying techniques and methods such as uni- and multi-
variate linear regression, cluster finding, Bayesian analysis, machine learning, and
time series analysis. Exploratory data analysis is a key aspect to get a sense of what
can be done and to maximize the insights that are gained from the data. Additionally,
emphasis is put on presentation-ready figures that are clear and easy to interpret.
What this learning path covers
Module 1, Getting Started with Python Data Analysis, shows how to work with time-
oriented data in Pandas. How do you clean, inspect, reshape, merge, or group data
– these are the concerns in this chapter. The library of choice in the course will be
Pandas again.
Module 2, Python Data Analysis Cookbook, demonstrates how to visualize
data and mentions frequently encountered pitfalls. Also, discusses
statistical probability distributions and correlation between two variables.
Module 3, Mastering Python Data Analysis, introduces linear, multiple, and logistic
regression with in-depth examples of using SciPy and stats models packages to test
various hypotheses of relationships between variables.
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