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Coursera Data Science Specialization

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Simply Statistics. Pages Basics.

Machine Learning

Datasciencectacontent/pml-ghPagesSetup.md at master · lgreski/datasciencectacontent. Leo Breiman. Leo Breiman passed away on July 5, 2005.

Leo Breiman

Professor Breiman was a member of the National Academy of Sciences. His research in later years focussed on computationally intensive multivariate analysis, especially the use of nonlinear methods for pattern recognition and prediction in high dimensional spaces. He was a co-author of Classification and Regression Trees and he developed decision trees as computationally efficient alternatives to neural nets. This work has applications in speech and optical character recognition. He was the author of the textbooks Probability and Stochastic Processes with a View Toward Applications, Statistics with a View Toward Applications, and Probability. Selected Papers Some papers are available. WALD Lectures Three pdf files are available from the WALD lectures, presented at the 277th meeting of the Institute of Mathematical Statistics, held in Banff, Alberta, Canada (July 28 to July 31, 2002). Machine LearningLooking Inside the Black BoxSoftware for the Masses.

A Community Site for R – Sponsored by Revolution Analytics. Home Page. Shiny - Tutorial. You can teach yourself to use Shiny in two ways.

Shiny - Tutorial

You can watch the “How to Start Shiny” webinar series, or you can work through the self-paced Shiny tutorial below. Who should take the tutorial? You will get the most out of the webinar or tutorial if you already know how to program in R, but not Shiny. If R is new to you, you may want to check out the learning resources at www.rstudio.com/training before taking this tutorial. If you are not sure whether you are ready for Shiny, try our quiz. If you use Shiny on a regular basis, you may want to skip this tutorial and visit the articles section of the Development Center. How to Start Shiny The How to Start Shiny webinar series is recorded here in a single video. View individual chapters The Shiny tutorial This seven lesson tutorial will take you from R programmer to Shiny developer. Each lesson includes an exercise. Click the Lesson 1 button to get started and say hello to Shiny! Continue to lesson 1. Home Page.

A deterministic statistical machine. As Roger pointed out the most recent batch of Y Combinator startups included a bunch of data-focused companies.

A deterministic statistical machine

One of these companies, StatWing, is a web-based tool for data analysis that looks like an improvement on SPSS with more plain text, more visualization, and a lot of the technical statistical details “under the hood”. I first read about StatWing on TechCrunch, where the title, “How Statwing Makes It Easier To Ask Questions About Data So You Don’t Have To Hire a Statistical Wizard”. StatWing looks super user-friendly and the idea of democratizing statistical analysis so more people can access these ideas is something that appeals to me. But, as one of the aforementioned statistical wizards, this had me freaked out for a minute. Once I looked at the software though, I realized it suffers from the same problem that most “user-friendly” statistical software suffers from.

The advantage is that people can get their data-related questions answered using a standard tool.

RR Peer 2

RPubs. Aggregate {stats} Compute Summary Statistics of Data Subsets Description Splits the data into subsets, computes summary statistics for each, and returns the result in a convenient form.

aggregate {stats}

Usage aggregate(x, ...) ## S3 method for class 'default': aggregate((x, ...)) ## S3 method for class 'data.frame': aggregate((x, by, FUN, ..., simplify = TRUE)) ## S3 method for class 'formula': aggregate((formula, data, FUN, ..., subset, na.action = na.omit)) ## S3 method for class 'ts': aggregate((x, nfrequency = 1, FUN = sum, ndeltat = 1, ts.eps = getOption("ts.eps"), ...)) Arguments x an R object. by.

RR Peer 1

Data Science Specialization. Read Statistical inference for data science. About this book This book is written as a companion book to the Statistical Inference Coursera class as part of the Data Science Specialization.

Read Statistical inference for data science

However, if you do not take the class, the book mostly stands on its own. A useful component of the book is a series of YouTube videos that comprise the Coursera class. The book is intended to be a low cost introduction to the important field of statistical inference. Bcaffo/courses. Coursera Wiki. Practice_assignment/practice_assignment.rmd at master · rdpeng/practice_assignment.

Software

Bootcamp. Machine Learning & Statistical Learning. Book. Elements of Data Analytic… by Jeff Leek. DataScienceSpecialization/DataScienceSpecialization.github.io. Data Science Specialization.