The Python Guide for New Data Scientists - Strickland, Jeffrey

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Résumé :
The Python Guide for New Data Scientists is written for someone who has graduated from college and has either had some discipline related course or has a desire to learn some of the basic data science building blocks. Data science is interdisciplinary, and I have work with people who have backgrounds in business or economics and are competent data scientists with some additional coursework. Ordinarily, we look for potential data scientists who have degrees in statistics, computer science, econometrics, information technology, operations research, mathematics, or engineering. That encompasses a wide range of disciplines. People who become data scientists generally have coursework in statistics, data analysis, basic programming, and college mathematics. During or after college, they have been exposed to machine learning models and prediction, R or Python programming, and some data wrangling. This book is designed to help with the latter. We'll cover basic data science tools and Python programming with Jupyter Lab. We'll cover getting and cleaning data, data preprocessing, exploratory data analysis (EDA), inferential statistics, regression models, generalized linear models, machine learning and prediction using random forests, and other algorithms. There is Python coding in every chapter, with many examples. Leaning the content is driven by very involved examples, including some using COVID-19 data. You'll find data scientists at banks, insurance companies, railroads, hospitals, utilities, and pharmaceutical companies. They work at Google, Amazon, Facebook, Netflix, Wal-Mart, Caterpillar. They are employed by the Department of Transportation (DoT), the Federal Bureau of Investigation (FBI), the Centers for Disease Control (CDC), the National Aeronautics and Space Administration (NASA). and the Department of Defense (DoD). All source code and markdown are in my GitHub repositories (there are 32 repositories) and are accessible to the public. We have a chapter covering GitHub

Biographie:
LinkedIn: https://www.linkedin.com/in/jeffreystrickland/ Related Books by the Author: Data Science Applications using R. Copyright (c) 2017. Jeffrey Strickland. Lulu, Inc. ISBN 978-0-359-81042-0, Predictive Crime Analysis using R. Copyright(c) 2018, Jeffrey Strickland. Lulu, Inc. ISBN 978-0-359-43159-5, Logistic Regression - Inside-Out. Copyright (c) 2017 by Jeffrey S. Strickland. Glasstree, Inc. ISBN 978-1-365-27041-3, Time Series Analysis using Open-Source Tools. Copyright(c) 2016, Jeffrey Strickland Glasstree, Inc. ISBN 978-1-5342-0100-2 , Predictive Analytics using R. Copyright (c) 2016 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-312-84101-7, Data Analytics Using Open-Source Tools. Copyright (c) 2015 by Jeffrey Strickland. Lulu Inc. ISBN 978-1-365-21384-7, Data Science and Analytics for Ordinary People. Copyright (c) 2015 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-329-28062-5, Operations Research using Open-Source Tools. Copyright (c) 2015 by Jeffrey Strickland. Lulu Inc. ISBN 978-1-329-00404-7, Missile Flight Simulation - Surface-to-Air Missiles, 2nd Edition. Copyright (c) 2015 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-329-64495-3, Verification and Validation for Modeling and Simulation Copyright (c) 2014 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-312-74061-7, Mathematical Modeling of Warfare and Combat Phenomenon. Copyright (c) 2011 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-45839255-8 , Simulation Conceptual Modeling. Copyright (c) 2011 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-105-18162-7, Discrete Event Simulation using ExtendSim 8. Copyright (c) 2010 by Jeffrey S. Strickland. Lulu.com. ISBN 978-0-557-72821-3...

Sommaire:
The Python Guide for New Data Scientists is written for someone who has graduated from college and has either had some discipline related course or has a desire to learn some of the basic data science building blocks. Data science is interdisciplinary, and I have work with people who have backgrounds in business or economics and are competent data scientists with some additional coursework. Ordinarily, we look for potential data scientists who have degrees in statistics, computer science, econometrics, information technology, operations research, mathematics, or engineering. That encompasses a wide range of disciplines. People who become data scientists generally have coursework in statistics, data analysis, basic programming, and college mathematics. During or after college, they have been exposed to machine learning models and prediction, R or Python programming, and some data wrangling. This book is designed to help with the latter. We'll cover basic data science tools and Python programming with Jupyter Lab. We'll cover getting and cleaning data, data preprocessing, exploratory data analysis (EDA), inferential statistics, regression models, generalized linear models, machine learning and prediction using random forests, and other algorithms. There is Python coding in every chapter, with many examples. Leaning the content is driven by very involved examples, including some using COVID-19 data. You'll find data scientists at banks, insurance companies, railroads, hospitals, utilities, and pharmaceutical companies. They work at Google, Amazon, Facebook, Netflix, Wal-Mart, Caterpillar. They are employed by the Department of Transportation (DoT), the Federal Bureau of Investigation (FBI), the Centers for Disease Control (CDC), the National Aeronautics and Space Administration (NASA). and the Department of Defense (DoD). All source code and markdown are in my GitHub repositories (there are 32 repositories) and are accessible to the public. We have a chapter covering GitHub...