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Data Science Using Python and R

By Chantal D. Larose , Daniel T. Larose
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  1. Free Chapter
    ABOUT THE AUTHORS
About this book
Data science is hot. Bloomberg named a data scientist as the ‘hottest job in America’. Python and R are the top two open-source data science tools using which you can produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Each chapter in the book presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. You’ll learn how to prepare data, perform exploratory data analysis, and prepare to model the data. As you progress, you’ll explore what are decision trees and how to use them. You’ll also learn about model evaluation, misclassification costs, naïve Bayes classification, and neural networks. The later chapters provide comprehensive information about clustering, regression modeling, dimension reduction, and association rules mining. The book also throws light on exciting new topics, such as random forests and general linear models. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. By the end of this book, you’ll have enough knowledge and confidence to start providing solutions to data science problems using R and Python.
Publication date:
April 2019
Publisher
Packt
Pages
256
ISBN
9781119526810

 

ABOUT THE AUTHORS

Chantal D. Larose, PhD, and Daniel T. Larose, PhD, form a unique father–daughter pair of data scientists. This is their third book as coauthors. Previously, they wrote:

  • Data Mining and Predictive Analytics, Second Edition, Wiley, 2015.
    • This 800‐page tome would be a wonderful companion to this book, for those looking to dive deeper in to the field.
  • Discovering Knowledge in Data: An Introduction to Data Mining, Second Edition, Wiley, 2014.

Chantal D. Larose completed her PhD in Statistics at the University of Connecticut in 2015, with dissertation Model‐Based Clustering of Incomplete Data. As an Assistant Professor of Decision Science at SUNY, New Paltz, she helped develop the Bachelor of Science in Business Analytics. Now, as an Assistant Professor of Statistics and Data Science at Eastern Connecticut State University, she is helping to develop the Mathematical Science Department's data science curriculum.

Daniel T. Larose completed his PhD in Statistics at the University of Connecticut in 1996, with dissertation Bayesian Approaches to Meta‐Analysis. He is a Professor of Statistics and Data Science at Central Connecticut State University. In 2001, he developed the world's first online Master of Science in Data Mining. This is the 12th textbook that he has authored or coauthored. He runs a small consulting business, DataMiningConsultant.com. He also directs the online Master of Data Science program at CCSU.

About the Authors
  • Chantal D. Larose

    Chantal D. Larose, Ph.D., is an assistant professor of statistics & data science at Eastern Connecticut State University (ECSU). She has co-authored three books on data science and predictive analytics and helped develop data science programs at ECSU and SUNY New Paltz. Her Ph.D. dissertation - model-based clustering of incomplete data - tackles the persistent problem of trying to do data science with incomplete data.

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  • Daniel T. Larose

    Daniel T. Larose, Ph.D., is a professor of data science and statistics and director of the data science programs at Central Connecticut State University. He has published many books on data science, data mining, predictive analytics, and statistics. His consulting clients include The Economist magazine, Forbes Magazine, the CIT Group, and Microsoft.

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Data Science Using Python and R
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