Tashman Adam P. - From Concepts To Code. Introduction To Data Science.pdf

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From Concepts to Code
The breadth of problems that can be solved with data science is astonishing, and
this book provides the required tools and skills to a broad audience. The reader
takes a journey into the forms, uses, and abuses of data and models, and learns
how to critically examine each step. Python coding and data analysis skills are
built from the ground up, with no prior coding experience assumed. The neces-
sary background in computer science, mathematics, and statistics is provided in
an approachable manner.
Each step of the machine learning lifecycle is discussed, from business objective
planning to monitoring a model in production. This end-to-end approach sup-
plies the broad view necessary to sidestep many of the pitfalls that can sink a data
science project. Detailed examples are provided from a wide range of applica-
tions and fields, from fraud detection in banking to breast cancer classification in
healthcare. The reader will learn the techniques to accomplish tasks that include
predicting outcomes, explaining observations, and detecting patterns. Improper
use of data and models can introduce unwanted effects and dangers to society.
A chapter on model risk provides a framework for comprehensively challenging
a model and mitigating weaknesses. When data is collected, stored, and used, it
may misrepresent reality and introduce bias. Strategies for addressing bias are
discussed.
From Concepts to Code: Introduction to Data Science
leverages content
developed by the author for a full-year data science course suitable for advanced
high school or early undergraduate students. This course is freely available and it
includes weekly lesson plans.
Adam P. Tashman
has been working in data science for more than 20 years. He is
an associate professor of data science at the University of Virginia School of Data
Science. He is currently Director of the Capstone program, and he was formerly
Director of the Online Master’s of Data Science program. He was the School of
Data Science Capital One Fellow for the 2023–2024 academic year. Dr. Tashman
won multiple awards from Amazon Web Services, where he advised education
and government technology companies on best practices in machine learning
and artificial intelligence. He lives in Charlottesville, VA with his wonderful wife
Elle and daughter Callie.
From Concepts to Code
Introduction to Data Science
Adam P. Tashman
Designed cover image: © Adam P. Tashman
First edition published 2024
by CRC Press
2385 NW Executive Center Drive, Suite 320, Boca Raton FL 33431
and by CRC Press
4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
CRC Press is an imprint of Taylor & Francis Group, LLC
© 2024 Adam P. Tashman
Reasonable efforts have been made to publish reliable data and information, but the author and pub-
lisher cannot assume responsibility for the validity of all materials or the consequences of their use.
The authors and publishers have attempted to trace the copyright holders of all material reproduced
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we may rectify in any future reprint.
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transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or
hereafter invented, including photocopying, microfilming, and recording, or in any information stor-
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Trademark notice:
Product or corporate names may be trademarks or registered trademarks and are
used only for identification and explanation without intent to infringe.
Library of Congress Cataloging-in-Publication Data
Names: Tashman, Adam P., author.
Title: From concepts to code : introduction to data science / Adam P.
Tashman.
Description: First edition. | Boca Raton FL : CRC Press, 2024. | Includes
bibliographical references and index.
Identifiers: LCCN 2023049717 (print) | LCCN 2023049718 (ebook) | ISBN
9781032517957 (hardback) | ISBN 9781032517988 (paperback) | ISBN
9781003403982 (ebook)
Subjects: LCSH: Data mining. | Big data. | Electronic data processing.
Classification: LCC QA76.9.D343 .T3635 2024 (print) | LCC QA76.9.D343
(ebook) | DDC 005.7--dc23/eng/20240220
LC record available at
https://lccn.loc.gov/2023049717
LC ebook record available at
https://lccn.loc.gov/2023049718
ISBN: 978-1-032-51795-7 (hbk)
ISBN: 978-1-032-51798-8 (pbk)
ISBN: 978-1-003-40398-2 (ebk)
DOI:
10.1201/9781003403982
Typeset in CMR10
by KnowledgeWorks Global Ltd.
Publisher’s note:
This book has been prepared from camera-ready copy provided by the authors.
Access the Support Material:
https://github.com/PredictioNN/intro_data_science_course/

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