Data Science For Mathematicians

Data Science For Mathematicians Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Data Science For Mathematicians book. This book definitely worth reading, it is an incredibly well-written.

Data Science for Mathematicians

Nathan Carter
Data Science for Mathematicians Book PDF

Author : Nathan Carter
Publisher : CRC Press
Page : 528 pages
File Size : 42,6 Mb
Release : 2020-09-15
Category : Mathematics
ISBN : 9780429675683

Get Book

Data Science for Mathematicians by Nathan Carter Pdf

Mathematicians have skills that, if deepened in the right ways, would enable them to use data to answer questions important to them and others, and report those answers in compelling ways. Data science combines parts of mathematics, statistics, computer science. Gaining such power and the ability to teach has reinvigorated the careers of mathematicians. This handbook will assist mathematicians to better understand the opportunities presented by data science. As it applies to the curriculum, research, and career opportunities, data science is a fast-growing field. Contributors from both academics and industry present their views on these opportunities and how to advantage them.

Data Science for Mathematicians

Nathan C. Carter
Data Science for Mathematicians Book PDF

Author : Nathan C. Carter
Publisher : Unknown
Page : 128 pages
File Size : 54,8 Mb
Release : 2020
Category : Big data
ISBN : 0367528495

Get Book

Data Science for Mathematicians by Nathan C. Carter Pdf

"Mathematicians have skills that, if deepened in the right ways, would enable them to use data to answer questions important to them and others, and report those answers in compelling ways. Data science combines parts of mathematics, statistics, computer science. Gaining such power and the ability to teach has reinvigorated the careers of mathematicians. This handbook will assist mathematicians to better understand the opportunities presented by data science"--

The Mathematics of Data

Michael W. Mahoney,John C. Duchi,Anna C. Gilbert
The Mathematics of Data Book PDF

Author : Michael W. Mahoney,John C. Duchi,Anna C. Gilbert
Publisher : American Mathematical Soc.
Page : 325 pages
File Size : 48,6 Mb
Release : 2018-11-15
Category : Big data
ISBN : 9781470435752

Get Book

The Mathematics of Data by Michael W. Mahoney,John C. Duchi,Anna C. Gilbert Pdf

Nothing provided

Mathematics for Machine Learning

Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Mathematics for Machine Learning Book PDF

Author : Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Publisher : Cambridge University Press
Page : 391 pages
File Size : 51,8 Mb
Release : 2020-04-23
Category : Computers
ISBN : 9781108470049

Get Book

Mathematics for Machine Learning by Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong Pdf

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Data Science and Machine Learning

Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman
Data Science and Machine Learning Book PDF

Author : Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman
Publisher : CRC Press
Page : 538 pages
File Size : 44,7 Mb
Release : 2019-11-20
Category : Business & Economics
ISBN : 9781000730777

Get Book

Data Science and Machine Learning by Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman Pdf

Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

High-Dimensional Probability

Roman Vershynin
High Dimensional Probability Book PDF

Author : Roman Vershynin
Publisher : Cambridge University Press
Page : 299 pages
File Size : 40,7 Mb
Release : 2018-09-27
Category : Business & Economics
ISBN : 9781108415194

Get Book

High-Dimensional Probability by Roman Vershynin Pdf

An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

Mathematical Foundations for Data Analysis

Jeff M. Phillips
Mathematical Foundations for Data Analysis Book PDF

Author : Jeff M. Phillips
Publisher : Springer Nature
Page : 299 pages
File Size : 42,6 Mb
Release : 2021-03-29
Category : Mathematics
ISBN : 9783030623418

Get Book

Mathematical Foundations for Data Analysis by Jeff M. Phillips Pdf

This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.

Doing Data Science

Cathy O'Neil,Rachel Schutt
Doing Data Science Book PDF

Author : Cathy O'Neil,Rachel Schutt
Publisher : "O'Reilly Media, Inc."
Page : 408 pages
File Size : 41,5 Mb
Release : 2013-10-09
Category : Computers
ISBN : 9781449363895

Get Book

Doing Data Science by Cathy O'Neil,Rachel Schutt Pdf

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

Foundations of Data Science

Avrim Blum,John Hopcroft,Ravindran Kannan
Foundations of Data Science Book PDF

Author : Avrim Blum,John Hopcroft,Ravindran Kannan
Publisher : Cambridge University Press
Page : 433 pages
File Size : 43,9 Mb
Release : 2020-01-23
Category : Computers
ISBN : 9781108485067

Get Book

Foundations of Data Science by Avrim Blum,John Hopcroft,Ravindran Kannan Pdf

Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.

Statistical Learning with Math and Python

Joe Suzuki
Statistical Learning with Math and Python Book PDF

Author : Joe Suzuki
Publisher : Springer Nature
Page : 261 pages
File Size : 53,7 Mb
Release : 2021-08-03
Category : Computers
ISBN : 9789811578779

Get Book

Statistical Learning with Math and Python by Joe Suzuki Pdf

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.

Weapons of Math Destruction

Cathy O'Neil
Weapons of Math Destruction Book PDF

Author : Cathy O'Neil
Publisher : Crown Publishing Group (NY)
Page : 274 pages
File Size : 45,6 Mb
Release : 2016
Category : Business & Economics
ISBN : 9780553418811

Get Book

Weapons of Math Destruction by Cathy O'Neil Pdf

"A former Wall Street quantitative analyst sounds an alarm on mathematical modeling, a pervasive new force in society that threatens to undermine democracy and widen inequality,"--NoveList.

The Big R-Book

Philippe J. S. De Brouwer
The Big R Book Book PDF

Author : Philippe J. S. De Brouwer
Publisher : John Wiley & Sons
Page : 928 pages
File Size : 46,8 Mb
Release : 2020-10-27
Category : Mathematics
ISBN : 9781119632726

Get Book

The Big R-Book by Philippe J. S. De Brouwer Pdf

Introduces professionals and scientists to statistics and machine learning using the programming language R Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices. Provides a practical guide for non-experts with a focus on business users Contains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reporting Uses a practical tone and integrates multiple topics in a coherent framework Demystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in R Shows readers how to visualize results in static and interactive reports Supplementary materials includes PDF slides based on the book’s content, as well as all the extracted R-code and is available to everyone on a Wiley Book Companion Site The Big R-Book is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.

Geometric Data Analysis

Brigitte Le Roux,Henry Rouanet
Geometric Data Analysis Book PDF

Author : Brigitte Le Roux,Henry Rouanet
Publisher : Springer Science & Business Media
Page : 475 pages
File Size : 49,8 Mb
Release : 2006-01-16
Category : Mathematics
ISBN : 9781402022364

Get Book

Geometric Data Analysis by Brigitte Le Roux,Henry Rouanet Pdf

Geometric Data Analysis (GDA) is the name suggested by P. Suppes (Stanford University) to designate the approach to Multivariate Statistics initiated by Benzécri as Correspondence Analysis, an approach that has become more and more used and appreciated over the years. This book presents the full formalization of GDA in terms of linear algebra - the most original and far-reaching consequential feature of the approach - and shows also how to integrate the standard statistical tools such as Analysis of Variance, including Bayesian methods. Chapter 9, Research Case Studies, is nearly a book in itself; it presents the methodology in action on three extensive applications, one for medicine, one from political science, and one from education (data borrowed from the Stanford computer-based Educational Program for Gifted Youth ). Thus the readership of the book concerns both mathematicians interested in the applications of mathematics, and researchers willing to master an exceptionally powerful approach of statistical data analysis.

Communicating with Data

Deborah Nolan,Sara Stoudt
Communicating with Data Book PDF

Author : Deborah Nolan,Sara Stoudt
Publisher : Oxford University Press
Page : 400 pages
File Size : 49,8 Mb
Release : 2021-03-25
Category : Science
ISBN : 9780192607508

Get Book

Communicating with Data by Deborah Nolan,Sara Stoudt Pdf

Communication is a critical yet often overlooked part of data science. Communicating with Data aims to help students and researchers write about their insights in a way that is both compelling and faithful to the data. General advice on science writing is also provided, including how to distill findings into a story and organize and revise the story, and how to write clearly, concisely, and precisely. This is an excellent resource for students who want to learn how to write about scientific findings, and for instructors who are teaching a science course in communication or a course with a writing component. Communicating with Data consists of five parts. Part I helps the novice learn to write by reading the work of others. Part II delves into the specifics of how to describe data at a level appropriate for publication, create informative and effective visualizations, and communicate an analysis pipeline through well-written, reproducible code. Part III demonstrates how to reduce a data analysis to a compelling story and organize and write the first draft of a technical paper. Part IV addresses revision; this includes advice on writing about statistical findings in a clear and accurate way, general writing advice, and strategies for proof reading and revising. Part V offers advice about communication strategies beyond the page, which include giving talks, building a professional network, and participating in online communities. This book also provides 22 portfolio prompts that extend the guidance and examples in the earlier parts of the book and help writers build their portfolio of data communication.

Computational Statistics in Data Science

Richard A. Levine,Walter W. Piegorsch,Hao Helen Zhang,Thomas C. M. Lee
Computational Statistics in Data Science Book PDF

Author : Richard A. Levine,Walter W. Piegorsch,Hao Helen Zhang,Thomas C. M. Lee
Publisher : John Wiley & Sons
Page : 672 pages
File Size : 51,6 Mb
Release : 2022-03-23
Category : Mathematics
ISBN : 9781119561088

Get Book

Computational Statistics in Data Science by Richard A. Levine,Walter W. Piegorsch,Hao Helen Zhang,Thomas C. M. Lee Pdf

Ein unverzichtbarer Leitfaden bei der Anwendung computergestützter Statistik in der modernen Datenwissenschaft In Computational Statistics in Data Science präsentiert ein Team aus bekannten Mathematikern und Statistikern eine fundierte Zusammenstellung von Konzepten, Theorien, Techniken und Praktiken der computergestützten Statistik für ein Publikum, das auf der Suche nach einem einzigen, umfassenden Referenzwerk für Statistik in der modernen Datenwissenschaft ist. Das Buch enthält etliche Kapitel zu den wesentlichen konkreten Bereichen der computergestützten Statistik, in denen modernste Techniken zeitgemäß und verständlich dargestellt werden. Darüber hinaus bietet Computational Statistics in Data Science einen kostenlosen Zugang zu den fertigen Einträgen im Online-Nachschlagewerk Wiley StatsRef: Statistics Reference Online. Außerdem erhalten die Leserinnen und Leser: * Eine gründliche Einführung in die computergestützte Statistik mit relevanten und verständlichen Informationen für Anwender und Forscher in verschiedenen datenintensiven Bereichen * Umfassende Erläuterungen zu aktuellen Themen in der Statistik, darunter Big Data, Datenstromverarbeitung, quantitative Visualisierung und Deep Learning Das Werk eignet sich perfekt für Forscher und Wissenschaftler sämtlicher Fachbereiche, die Techniken der computergestützten Statistik auf einem gehobenen oder fortgeschrittenen Niveau anwenden müssen. Zudem gehört Computational Statistics in Data Science in das Bücherregal von Wissenschaftlern, die sich mit der Erforschung und Entwicklung von Techniken der computergestützten Statistik und statistischen Grafiken beschäftigen.