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Primer for data analytics and gradua...

Schneider, Grant.

Primer for data analytics and graduate study in statistics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Primer for data analytics and graduate study in statisticsby Douglas Wolfe, Grant Schneider.
Author:	Wolfe, Douglas.
other author:	Schneider, Grant.
Published:	Cham :Springer International Publishing :2020.
Description:	x, 233 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Mathematical statistics.
Online resource:	https://doi.org/10.1007/978-3-030-47479-9
ISBN:	9783030474799$q(electronic bk.)

Primer for data analytics and graduate study in statistics
Wolfe, Douglas.

Primer for data analytics and graduate study in statistics[electronic resource] /by Douglas Wolfe, Grant Schneider. - Cham :Springer International Publishing :2020. - x, 233 p. :ill., digital ;24 cm.

This book is specially designed to refresh and elevate the level of understanding of the foundational background in probability and distributional theory required to be successful in a graduate-level statistics program. Advanced undergraduate students and introductory graduate students from a variety of quantitative backgrounds will benefit from the transitional bridge that this volume offers, from a more generalized study of undergraduate mathematics and statistics to the career-focused, applied education at the graduate level. In particular, it focuses on growing fields that will be of potential interest to future M.S. and Ph.D. students, as well as advanced undergraduates heading directly into the workplace: data analytics, statistics and biostatistics, and related areas.

ISBN: 9783030474799$q(electronic bk.)

Standard No.: 10.1007/978-3-030-47479-9doiSubjects--Topical Terms:

181877
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Primer for data analytics and graduate study in statistics
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520 $a This book is specially designed to refresh and elevate the level of understanding of the foundational background in probability and distributional theory required to be successful in a graduate-level statistics program. Advanced undergraduate students and introductory graduate students from a variety of quantitative backgrounds will benefit from the transitional bridge that this volume offers, from a more generalized study of undergraduate mathematics and statistics to the career-focused, applied education at the graduate level. In particular, it focuses on growing fields that will be of potential interest to future M.S. and Ph.D. students, as well as advanced undergraduates heading directly into the workplace: data analytics, statistics and biostatistics, and related areas.
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