Primer for data analytics and graduate study in statistics
Material type:
TextPublication details: Switzerland Springer 2020 Description: x, 233 pISBN: 9783030474812Subject(s): Mathematical statistics | Probabilities | Data structures (Computer science)DDC classification: 519.5 Summary: About this book
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.
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
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Indian Institute of Management LRC General Stacks | IT & Decisions Sciences | 519.5 WOL (Browse shelf(Opens below)) | 1 | Available | 003529 |
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| 519.5 ACZ Complete business statistics | 519.5 BER Guide to intelligent data science: how to intelligently make use of real data | 519.5 WEI Statistics using R: | 519.5 WOL Primer for data analytics and graduate study in statistics | 519.50285 SAI Introduction to data analysis in R: | 519.502855133 GIL A guide to R for social and behavioral science statistics | 519.502855133 GRO Hands-on programming with R: write your own functions and simulations |
About this book
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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