Samir Madhavan - Mastering Python for data science : explore the world of data science through Python and learn how to make sense of data
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- Book:Mastering Python for data science : explore the world of data science through Python and learn how to make sense of data
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Explore the world of data science through Python and learn how to make sense of data
About This Book- Master data science methods using Python and its libraries
- Create data visualizations and mine for patterns
- Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning
If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed.
What You Will Learn- Manage data and perform linear algebra in Python
- Derive inferences from the analysis by performing inferential statistics
- Solve data science problems in Python
- Create high-end visualizations using Python
- Evaluate and apply the linear regression technique to estimate the relationships among variables.
- Build recommendation engines with the various collaborative filtering algorithms
- Apply the ensemble methods to improve your predictions
- Work with big data technologies to handle data at scale
Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving.
This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science.
Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods.
Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics.
Style and approachThis book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.
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Copyright 2015 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: August 2015
Production reference: 1260815
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78439-015-0
www.packtpub.com
Author
Samir Madhavan
Reviewers
Sbastien Celles
Robert Dempsey
Maurice HT Ling
Ratanlal Mahanta
Yingssu Tsai
Commissioning Editor
Pramila Balan
Acquisition Editor
Sonali Vernekar
Content Development Editor
Arun Nadar
Technical Editor
Chinmay S. Puranik
Copy Editor
Sonia Michelle Cheema
Project Coordinator
Neha Bhatnagar
Proofreader
Safis Editing
Indexer
Monica Ajmera Mehta
Graphics
Disha Haria
Jason Monteiro
Production Coordinator
Arvindkumar Gupta
Cover Work
Arvindkumar Gupta
Samir Madhavan has been working in the field of data science since 2010. He is an industry expert on machine learning and big data. He has also reviewed R Machine Learning Essentials by Packt Publishing. He was part of the ubiquitous Aadhar project of the Unique Identification Authority of India, which is in the process of helping every Indian get a unique number that is similar to a social security number in the United States. He was also the first employee of Flutura Decision Sciences and Analytics and is a part of the core team that has helped scale the number of employees in the company to 50. His company is now recognized as one of the most promising Internet of ThingsDecision Sciences companies in the world.
I would like to thank my mom, Rajasree Madhavan, and dad, P Madhavan, for all their support. I would also like to thank Srikanth Muralidhara, Krishnan Raman, and Derick Jose, who gave me the opportunity to start my career in the world of data science.
Sbastien Celles is a professor of applied physics at Universite de Poitiers (working in the thermal science department). He has used Python for numerical simulations, data plotting, data predictions, and various other tasks since the early 2000s. He is a member of PyData and was granted commit rights to the pandas DataReader project. He is also involved in several open source projects in the scientific Python ecosystem.
Sebastien is also the author of some Python packages available on PyPi, which are as follows:
- openweathermap_requests: This is a package used to fetch data from OpenWeatherMap.org using Requests and Requests-cache and to get pandas DataFrame with weather history
- pandas_degreedays: This is a package used to calculate degree days (a measure of heating or cooling) from the pandas time series of temperature
- pandas_confusion: This is a package used to manage confusion matrices, plot and binarize them, and calculate overall and class statistics
- There are some other packages authored by him, such as pyade, pandas_datareaders_unofficial, and more
He also has a personal interest in data mining, machine learning techniques, forecasting, and so on. You can find more information about him at http://www.celles.net/wiki/Contact or https://www.linkedin.com/in/sebastiencelles.
Robert Dempsey is a leader and technology professional, specializing in delivering solutions and products to solve tough business challenges. His experience of forming and leading agile teams combined with more than 15 years of technology experience enables him to solve complex problems while always keeping the bottom line in mind.
Robert founded and built three start-ups in the tech and marketing fields, developed and sold two online applications, consulted for Fortune 500 and Inc. 500 companies, and has spoken nationally and internationally on software development and agile project management.
He's the founder of Data Wranglers DC, a group dedicated to improving the craft of data wrangling, as well as a board member of Data Community DC. He is currently the team leader of data operations at ARPC, an econometrics firm based in Washington, DC.
In addition to spending time with his growing family, Robert geeks out on Raspberry Pi's, Arduinos, and automating more of his life through hardware and software.
Maurice HT Ling has been programming in Python since 2003. Having completed his PhD in bioinformatics and BSc (Hons) in molecular and cell biology from The University of Melbourne, he is currently a research fellow at Nanyang Technological University, Singapore. He is also an honorary fellow of The University of Melbourne, Australia. Maurice is the chief editor of Computational and Mathematical Biology and coeditor of The Python Papers. Recently, he cofounded the first synthetic biology start-up in Singapore, called AdvanceSyn Pte. Ltd., as the director and chief technology officer. His research interests lie in life itself, such as biological life and artificial life, and artificial intelligence, which use computer science and statistics as tools to understand life and its numerous aspects. In his free time, Maurice likes to read, enjoy a cup of coffee, write his personal journal, or philosophize on various aspects of life. His website and LinkedIn profile are http://maurice.vodien.com and http://www.linkedin.com/in/mauriceling, respectively.
Ratanlal Mahanta is a senior quantitative analyst. He holds an MSc degree in computational finance and is currently working at GPSK Investment Group as a senior quantitative analyst. He has 4 years of experience in quantitative trading and strategy development for sell-side and risk consultation firms. He is an expert in high frequency and algorithmic trading.
He has expertise in the following areas:
- Quantitative trading: This includes FX, equities, futures, options, and engineering on derivatives
- Algorithms: This includes Partial Differential Equations, Stochastic Differential Equations, Finite Difference Method, Monte-Carlo, and Machine Learning
- Code: This includes R Programming, C++, Python, MATLAB, HPC, and scientific computing
- Data analysis: This includes big data analytics (EOD to TBT), Bloomberg, Quandl, and Quantopian
- Strategies: This includes Vol Arbitrage, Vanilla and Exotic Options Modeling, trend following, Mean reversion, Co-integration, Monte-Carlo Simulations, ValueatRisk, Stress Testing, Buy side trading strategies with high Sharpe ratio, Credit Risk Modeling, and Credit Rating
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