Mastering Python Data Analysis

Become an expert at using Python for advanced statistical analysis of data using real-world examples
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Mastering Python Data Analysis

Magnus Vilhelm Persson, Luiz Felipe Martins

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Become an expert at using Python for advanced statistical analysis of data using real-world examples
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Book Details

ISBN 139781783553297
Paperback284 pages

Book Description

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

Chapter 1: Tools of the Trade
Before you start
Using the notebook interface
Imports
An example using the Pandas library
Summary
Chapter 2: Exploring Data
The General Social Survey
Univariate data
Relationships between variables – scatterplots
Summary
Chapter 3: Learning About Models
Models and experiments
The cumulative distribution function
Working with distributions
The probability density function
Where do models come from?
Multivariate distributions
Summary
Chapter 4: Regression
Introducing linear regression
Multivariate regression
Logistic regression
Summary
Chapter 5: Clustering
Introduction to cluster finding
K-means clustering
Hierarchical clustering analysis
Summary
Chapter 6: Bayesian Methods
The Bayesian method
U.S. air travel safety record
Climate change - CO in the atmosphere
Summary
Chapter 7: Supervised and Unsupervised Learning
Introduction to machine learning
Scikit-learn
Linear regression
Clustering
Seeds classification
Summary
Chapter 8: Time Series Analysis
Introduction
Pandas and time series data
Indexing and slicing
Resampling, smoothing, and other estimates
Stationarity
Patterns and components
Time series models
Summary

What You Will Learn

  • Read, sort, and map various data into Python and Pandas
  • Recognise patterns so you can understand and explore data
  • Use statistical models to discover patterns in data
  • Review classical statistical inference using Python, Pandas, and SciPy
  • Detect similarities and differences in data with clustering
  • Clean your data to make it useful
  • Work in Jupyter Notebook to produce publication ready figures to be included in reports

Authors

Table of Contents

Chapter 1: Tools of the Trade
Before you start
Using the notebook interface
Imports
An example using the Pandas library
Summary
Chapter 2: Exploring Data
The General Social Survey
Univariate data
Relationships between variables – scatterplots
Summary
Chapter 3: Learning About Models
Models and experiments
The cumulative distribution function
Working with distributions
The probability density function
Where do models come from?
Multivariate distributions
Summary
Chapter 4: Regression
Introducing linear regression
Multivariate regression
Logistic regression
Summary
Chapter 5: Clustering
Introduction to cluster finding
K-means clustering
Hierarchical clustering analysis
Summary
Chapter 6: Bayesian Methods
The Bayesian method
U.S. air travel safety record
Climate change - CO in the atmosphere
Summary
Chapter 7: Supervised and Unsupervised Learning
Introduction to machine learning
Scikit-learn
Linear regression
Clustering
Seeds classification
Summary
Chapter 8: Time Series Analysis
Introduction
Pandas and time series data
Indexing and slicing
Resampling, smoothing, and other estimates
Stationarity
Patterns and components
Time series models
Summary

Book Details

ISBN 139781783553297
Paperback284 pages
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