Book Image

Practical Data Analysis Cookbook

By : Tomasz Drabas
Book Image

Practical Data Analysis Cookbook

By: Tomasz Drabas

Overview of this book

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 (19 chapters)
Practical Data Analysis Cookbook
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Index

Identifying parts of speech, handling n-grams, and recognizing named entities


One of the first things that you might want to look at is recognizing parts of speech for a word; it is really fundamental to understand in a sentence that the word checks is a verb or noun.

This, as useful as it is, will not help you handle bigrams (or, more generally, n-grams): clusters of words that, if analyzed separately (in a certain context), would lead to improper understanding of the text. For example, consider a phrase neural networks in an article on machine learning and, more specifically, an application of neural networks to control packet scheduling and routing in a local network. In the same article, these two words (neural and networks) can occur on their own with, to some degree, different meanings.

Finally, reading an article on politics at a recent meeting of the heads of states, we might encounter the word President quite frequently; what would be more interesting to understand is how many times...