Tomasz Drabas - Practical Data Analysis Cookbook
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Over 60 practical recipes on data exploration and analysis
About This Book
- Forecast the output of an electric plant and the water flow of American rivers using pandas, NumPy, Statsmodels, and scikit-learn
- Find and extract the most important features from your dataset using the most efficient Python libraries
Who This Book Is For
If you are a beginner or intermediate-level professional who is looking to solve your day-to-day, analytical problems with Python, this book is for you. Even with no prior programming and data analytics experience, you will be able to finish each recipe and learn while doing so.
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
In Detail
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 dataarguably 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.
Style and approach
This hands-on recipe guide is divided into three sections that tackle and overcome real-world data modeling problems faced by data analysts/scientist in their everyday work. Each independent recipe is written in...
Tomasz Drabas: author's other books
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Copyright 2016 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: April 2011
Production reference: 1250416
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78355-166-8
www.packtpub.com
Author
Tomasz Drabas
Reviewers
Brett Bloomquist
Khaled Tannir
Commissioning Editor
Dipika Gaonkar
Acquisition Editor
Prachi Bisht
Content Development Editor
Pooja Mhapsekar
Technical Editor
Bharat Patil
Copy Editor
Tasneem Fatehi
Project Coordinator
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Proofreader
Safis Editing
Indexer
Mariammal Chettiyar
Production Coordinator
Nilesh R. Mohite
Cover Work
Nilesh R. Mohite
Tomasz Drabas is a data scientist working for Microsoft and currently residing in the Seattle area. He has over 12 years of international experience in data analytics and data science in numerous fields, such as advanced technology, airlines, telecommunications, finance, and consulting.
Tomasz started his career in 2003 with LOT Polish Airlines in Warsaw, Poland, while finishing his master's degree in strategy management. In 2007, he moved to Sydney to pursue a doctoral degree in operations research at the University of New South Wales, School of Aviation; his research crossed boundaries between discrete choice modeling and airline operations research. During his time in Sydney, he worked as a data analyst for Beyond Analysis Australia and as a senior data analyst/data scientist for Vodafone Hutchison Australia, among others. He has also published scientific papers, attended international conferences, and served as a reviewer for scientific journals.
In 2015, he relocated to Seattle to begin his work for Microsoft. There he works on numerous projects involving solving problems in high-dimensional feature space.
First and foremost, I would like to thank my wife, Rachel, and daughter, Skye, for encouraging me to undertake this challenge and tolerating long days of developing code and late nights of writing up. You are the best and I love you beyond bounds! Also, thanks to my family for putting up with me (in general).
Tomasz Bednarz has not only been a great friend but also a great mentor when I was learning programmingthank you! I also want to thank my current and former managers, Mike Stephenson and Rory Carter, as well as numerous colleagues and friends who also encouraged me to finish this book.
Special thanks go to my two former supervisors, Dr Richard Cheng-Lung Wu and Dr Tomasz Jablonski. The master's project with Tomasz sparked my interest in neural networkslessons that I will never forget. Without Richard's help, I would not have been able to finish my PhD and will always be grateful for his help, guidance, and friendship.
Brett Bloomquist holds a BS in mathematics and an MS in computer science, specializing in computer-aided geometric design. He has 26 years of work experience in the software industry with a focus on geometric modeling algorithms and computer graphics. More recently, Brett has been applying his mathematics and visualization background as a principal data scientist.
Khaled Tannir is a visionary solution architect with more than 20 years of technical experience focusing on big data technologies, data science machine learning, and data mining since 2010.
He is widely recognized as an expert in these fields and has a bachelor's degree in electronics and a master's degree in system information architectures. He is working on completing his PhD.
Khaled has more than 15 certifications (R programming, big data, and many more) and is a Microsoft Certified Solution Developer (MCSD) and an avid technologist.
He has worked for many companies in France (and recently in Canada), leading the development and implementation of software solutions and giving technical presentations.
He is the author of the books RavenDB 2.x Beginner's Guide and Optimizing Hadoop MapReduce , both by Packt Publishing (which were translated in Simplified Chinese) and a technical reviewer on the books, Pentaho Analytics for MongoDB , MongoDB High Availability , and Learning Predictive Analytics with R , by Packt Publishing.
He enjoys taking landscape and night photos, traveling, playing video games, creating funny electronics gadgets using Arduino, Raspberry Pi, and .Net Gadgeteer, and of course spending time with his wife and family.
You can connect with him on LinkedIn or reach him at <>.
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Data analytics and data science have garnered a lot of attention from businesses around the world. The amount of data generated these days is mind-boggling, and it keeps growing everyday; with the proliferation of mobiles, access to Facebook, YouTube, Netflix, or other 4K video content providers, and increasing reliance on cloud computing, we can only expect this to increase.
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