You're reading from Mastering Python Data Analysis Become an expert at using Python for advanced statistical analysis of data using real-world examples

Product type Paperback

Published in Jun 2016

Publisher Packt

ISBN-13 9781783553297

Length 284 pages

Edition 1st Edition

Languages

Python

Tools

Pandas

Concepts

Data Analysis

Authors (2):

Magnus Vilhelm Persson

Luiz Felipe Martins

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Table of Contents (10) Chapters

Preface

1. Tools of the Trade FREE CHAPTER

2. Exploring Data

3. Learning About Models

4. Regression

5. Clustering

6. Bayesian Methods

7. Supervised and Unsupervised Learning

8. Time Series Analysis

A. More on Jupyter Notebook and matplotlib Styles

Chapter 3. Learning About Models

In the most generic sense, a model is an approximate description of a portion of reality. Models are essential to science and, in fact, any area of knowledge: it is only possible to comprehend the world by concentrating on a small part of it at a time and making suitable simplifications.

In this chapter, we will discuss the following topics:

Using basic models in data analysis
Using the cumulative distribution function and probability density function to characterize a variable
Using the preceding functions and various tools to make point estimates and generating random numbers with a certain distribution
Discussing examples of discrete and continuous random variables and an overview of multivariate distributions

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Authors (2)

Magnus Vilhelm Persson

Magnus Vilhelm Persson is a scientist with a passion for Python and open source software usage and development. He obtained his PhD in Physics/Astronomy from Copenhagen Universitys Centre for Star and Planet Formation (StarPlan) in 2013. Since then, he has continued his research in Astronomy at various academic institutes across Europe. In his research, he uses various types of data and analysis to gain insights into how stars are formed. He has participated in radio shows about Astronomy and also organized workshops and intensive courses about the use of Python for data analysis. You can check out his web page at http://vilhelm.nu.

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Luiz Felipe Martins

Luiz Felipe Martins holds a PhD in applied mathematics from Brown University and has worked as a researcher and educator for more than 20 years. His research is mainly in the field of applied probability. He has been involved in developing code for the open source homework system, WeBWorK, where he wrote a library for the visualization of systems of differential equations. He was supported by an NSF grant for this project. Currently, he is an Associate Professor in the Department of Mathematics at Cleveland State University, Cleveland, Ohio, where he has developed several courses in applied mathematics and scientific computing. His current duties include coordinating all first-year calculus sessions.

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