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Practical Data Analysis Cookbook

You're reading from   Practical Data Analysis Cookbook Over 60 practical recipes on data exploration and analysis

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Product type Paperback
Published in Apr 2016
Publisher
ISBN-13 9781783551668
Length 384 pages
Edition 1st Edition
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Author (1):
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Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Table of Contents (13) Chapters Close

Preface 1. Preparing the Data FREE CHAPTER 2. Exploring the Data 3. Classification Techniques 4. Clustering Techniques 5. Reducing Dimensions 6. Regression Methods 7. Time Series Techniques 8. Graphs 9. Natural Language Processing 10. Discrete Choice Models 11. Simulations Index

What you need for this book

For this book, you need a personal computer (it can be a Windows machine, Mac, or Linux) with an installed and configured Python 3.5 environment; we use the Anaconda distribution of Python that can be downloaded at https://www.continuum.io/downloads.

Throughout this book, we use various Python modules: pandas, NumPy/SciPy, SciKit-Learn, MLPY, StatsModels, PyBrain, NLTK, BeautifulSoup, Optunity, Matplotlib, Seaborn, Bokeh, PyLab, OpenPyXl, PyMongo, SQLAlchemy, NetworkX, and SimPy. Most of the modules used come preinstalled with Anaconda, but some of them need to be installed via either the conda installer or by downloading the module and using the python setup.py install command. It is fine if some of those modules are not currently installed on your machine; we will guide you through the installation process.

We also use several non-Python tools: OpenRefine to aid in data cleansing and analysis, D3.js to visualize data, Postgres and MongoDB databases to store data, Gephi to visualize graphs, and PythonBiogeme to estimate discrete choice models. We will provide detailed installation instructions where needed.

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