• Complain

Madhavan - Mastering Python for Data Science

Here you can read online Madhavan - Mastering Python for Data Science full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. City: Birmingham;England, year: 2015, publisher: Packt Publishing, genre: Computer. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:

Romance novel Science fiction Adventure Detective Science History Home and family Prose Art Politics Computer Non-fiction Religion Business Children Humor

Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.

Madhavan Mastering Python for Data Science
  • Book:
    Mastering Python for Data Science
  • Author:
  • Publisher:
    Packt Publishing
  • Genre:
  • Year:
    2015
  • City:
    Birmingham;England
  • Rating:
    5 / 5
  • Favourites:
    Add to favourites
  • Your mark:
    • 100
    • 1
    • 2
    • 3
    • 4
    • 5

Mastering Python for Data Science: summary, description and annotation

We offer to read an annotation, description, summary or preface (depends on what the author of the book "Mastering Python for Data Science" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.

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

      Who This Book Is For

      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...
  • Madhavan: author's other books


    Who wrote Mastering Python for Data Science? Find out the surname, the name of the author of the book and a list of all author's works by series.

    Mastering Python for Data Science — read online for free the complete book (whole text) full work

    Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Mastering Python for Data Science" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.

    Light

    Font size:

    Reset

    Interval:

    Bookmark:

    Make
    Mastering Python for Data Science

    Mastering Python for Data Science

    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

    Credits

    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

    About the Author

    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.

    About the Reviewers

    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
    Next page
    Light

    Font size:

    Reset

    Interval:

    Bookmark:

    Make

    Similar books «Mastering Python for Data Science»

    Look at similar books to Mastering Python for Data Science. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.


    Reviews about «Mastering Python for Data Science»

    Discussion, reviews of the book Mastering Python for Data Science and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.