Practical Data Analysis Cookbook

Over 60 practical recipes on data exploration and analysis

Practical Data Analysis Cookbook

Tomasz Drabas

6 customer reviews
Over 60 practical recipes on data exploration and analysis
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Book Details

ISBN 139781783551668
Paperback384 pages

Book Description

Data analysis is the process of systematically applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data. Its importance has been most visible in the sector of information and communication technologies. It is an employee asset in almost all economy sectors.

This book provides a rich set of independent recipes that dive into the world of data analytics and modeling using a variety of approaches, tools, and algorithms. You will learn the basics of data handling and modeling, and will build your skills gradually toward more advanced topics such as simulations, raw text processing, social interactions analysis, and more.

First, you will learn some easy-to-follow practical techniques on how to read, write, clean, reformat, explore, and understand your data—arguably the most time-consuming (and the most important) tasks for any data scientist.

In the second section, different independent recipes delve into intermediate topics such as classification, clustering, predicting, and more. With the help of these easy-to-follow recipes, you will also learn techniques that can easily be expanded to solve other real-life problems such as building recommendation engines or predictive models.

In the third section, you will explore more advanced topics: from the field of graph theory through natural language processing, discrete choice modeling to simulations. You will also get to expand your knowledge on identifying fraud origin with the help of a graph, scrape Internet websites, and classify movies based on their reviews.

By the end of this book, you will be able to efficiently use the vast array of tools that the Python environment has to offer.

Table of Contents

What You Will Learn

  • Read, clean, transform, and store your data usng Pandas and OpenRefine
  • Understand your data and explore the relationships between variables using Pandas and D3.js
  • Explore a variety of techniques to classify and cluster outbound marketing campaign calls data of a bank using Pandas, mlpy, NumPy, and Statsmodels
  • Reduce the dimensionality of your dataset and extract the most important features with pandas, NumPy, and mlpy
  • Predict the output of a power plant with regression models and forecast water flow of American rivers with time series methods using pandas, NumPy, Statsmodels, and scikit-learn
  • Explore social interactions and identify fraudulent activities with graph theory concepts using NetworkX and Gephi
  • Scrape Internet web pages using urlib and BeautifulSoup and get to know natural language processing techniques to classify movies ratings using NLTK
  • Study simulation techniques in an example of a gas station with agent-based modeling

Authors

Table of Contents

Book Details

ISBN 139781783551668
Paperback384 pages
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