Introduction to Stochastic Process With R

Learning Path: Introduction to Data Science with R  eBooks & eLearning

Posted by FenixN at Dec. 4, 2016
Learning Path: Introduction to Data Science with R

Learning Path: Introduction to Data Science with R
HDRips | MP4/AVC, ~372 kb/s | 1920х1080 / 1280x720 | Duration: 20:19:39 | English: AAC, 128 kb/s (2 ch) | 9.84 GB
Genre: Development / Programming

The R programming language has arguably become the single most important tool for computational statistics, visualization, and data science. With this Learning Path, master the basics that you'll need as a data scientist. You'll work your data like never before.
An Informal Introduction to Stochastic Calculus with Applications

An Informal Introduction to Stochastic Calculus with Applications by Ovidiu Calin
English | 2015 | ISBN: 9814678937, 9814689912 | 316 pages | PDF | 3,2 MB
An Introduction to Stochastic Processes with Applications to Biology, Second Edition

Linda J. S. Allen, "An Introduction to Stochastic Processes with Applications to Biology, Second Edition"
2011 | ISBN-10: 1439818827 | 490 pages | PDF | 4 MB
Informal Introduction to Stochastic Processes with Maple (repost)

Informal Introduction to Stochastic Processes with Maple (Universitext) by Jan Vrbik and Paul Vrbik
English | ISBN: 1461440564 | 2012 | PDF | 288 pages | 3 MB

The book presents an introduction to Stochastic Processes including Markov Chains, Birth and Death processes, Brownian motion and Autoregressive models. The emphasis is on simplifying both the underlying mathematics and the conceptual understanding of random processes.
Informal Introduction to Stochastic Processes with Maple (repost)

Informal Introduction to Stochastic Processes with Maple (Universitext) by Jan Vrbik and Paul Vrbik
English | ISBN: 1461440564 | 2012 | PDF | 288 pages | 3 MB

The book presents an introduction to Stochastic Processes including Markov Chains, Birth and Death processes, Brownian motion and Autoregressive models. The emphasis is on simplifying both the underlying mathematics and the conceptual understanding of random processes.
Introduction to Stochastic Calculus with Applications (repost)

Introduction to Stochastic Calculus with Applications by Fima C. Klebaner
English | 2005-06-30 | ISBN: 186094566X | 427 pages | DJVU | 2.6 mb

This book presents a concise treatment of stochastic calculus and its applications. It gives a simple but rigorous treatment of the subject including a range of advanced topics, it is useful for practitioners who use advanced theoretical results. It covers advanced applications, such as models in mathematical finance, biology and engineering.
Informal Introduction to Stochastic Processes with Maple (Repost)

Jan Vrbik, "Informal Introduction to Stochastic Processes with Maple"
English | ISBN: 1461440564 | 2012 | PDF | 288 pages | 3 MB
Introduction to Data Science with R: How to Manipulate, Visualize, and Model Data with the R Language

Introduction to Data Science with R: How to Manipulate, Visualize, and Model Data with the R Language
English | November 2014 | mp4 | H264 1920x1080 | AAC 1 ch 125 kbps | 4 hrs 26 min | 3.17 GB
eLearning
Introduction to Data Analysis with R for Forensic Scientists

James Michael Curran, "Introduction to Data Analysis with R for Forensic Scientists"
English | ISBN: 1420088262 | 2010 | 331 pages | PDF | 13 MB

An Introduction to Machine Learning with Web Data [repost]  eBooks & eLearning

Posted by FenixN at Nov. 28, 2016
An Introduction to Machine Learning with Web Data [repost]

An Introduction to Machine Learning with Web Data
HDRips | MP4/AVC, ~1200 kb/s | 1280x720 | Duration: 02:43:22 | English: AAC, 128 kb/s (2 ch) | 1.43 GB
Genre: Development / Programming

Once you've accumulated a pile of data through your web application, what do you do with it? In this insightful video course, bit.ly lead scientist Hilary Mason shows you how to solve data analysis problems using basic machine learning techniques and frameworks. You'll follow several examples through the entire process—from obtaining, cleaning, and exploring data to building a model and interpreting the results.