angus.readthedocs.io
Population Genetics Tutorial — angus 5.0 documentation
http://angus.readthedocs.io/en/2016/pop_gen_tutorial.html
Angus 5.0 documentation. Before you guys got here. Started with data from: “Genomic islands of speciation separate cichlid ecomorphs in an East African crater lake”, Malinsky et al 2015. Downloaded VCF from http:/ datadryad.org/resource/doi:10.5061/dryad.770mc. Http:/ datadryad.org/bitstream/handle/10255/dryad.101389/Massoko Dryad VCF final.vcf.gz. These data had been filtered for quality. And only variable sites had been retained. And phased using the program. Start your instance with at least 50 Gb....
andreasjungherr.net
Twitter | Too Bad You Never Knew Ace Hanna
http://andreasjungherr.net/tag/twitter
Too Bad You Never Knew Ace Hanna. Don't know where we're goin', but there's no sense bein' late. Analyzing Political Communication with Digital Trace Data (2015). Das Internet in Wahlkämpfen (2013). Using Digital Trace Data in the Social Sciences: Session 7 Extracting Data for Typical Analyses. Jul 20, ’16. After exporting the summary statistics ready for analysis, we load them into R to perform a series of typical analyses. You find introductory readings on using R. Exploratory data analysis in R. The e...
wzchen.com
23 Free Data Science Books
http://www.wzchen.com/data-science-books
23 Free Data Science Books. Last updated January 17, 2017. As a data scientist at Quora, I often get asked for my advice about becoming a data scientist. To help people exploring the data science career track, I've taken some time to compile my top recommendations of quality data science books. That are either available for free (legally, of course) or are Pay What You Want (PWYW) with $0 minimum. Please bookmark this place and refer to it often! Check price on Amazon. R Programming for Data Science,.
edhar.cas.msu.edu
MI 985 - TC 985 Analysis for Media
http://edhar.cas.msu.edu/schedule.html
TC 985 Analysis for Media. This schedule is tentative and may change at any time; please check back here often for updates. In particular, I expect the readings to change regularly; check back each week for new readings. Current issues in statistics. Cancelled: Martin Luther King day. P-Values and Hypothesis Tests. Intro to R, Rmarkdown, and Reproducible Statistics. Making arguments with statistics. Gelman 2; Six Rules. OLS: How it works. OLS: Interpretation: Continuous Predictors. Gelman 6,11,12.
jules32.github.io
Better science in less time using data science tools
https://jules32.github.io/opensci-talk/short
November 11, 2016 / WSN, Monterey, California. Trying to do reproducible science. Data final v2.xls. Re: FWD: data question. Sorry guys, this probably isnt reproducible science. Actually doing reproducible science. We struggled to reproduce and repeat our own work. Data science principles and tools have changed how we do science:. Upcoming paper: how we now do reproducible science by leveraging from data science - philosophy and tools. Lowndes et al., in prep. Grolemund and Wickham 2016.
jules32.github.io
Software Carpentry Workshop at Oxford University
http://jules32.github.io/2016-07-12-Oxford/overview
Software Carpentry Workshop at Oxford University. Workshop Overview: Reproducible Science with RStudio and GitHub. Julie Stewart Lowndes, twitter: @juliesquid. July 12, 2016. Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in the mundane labor of collecting and preparing data, before it can be explored for useful information. - NYTimes (2014). Googling is a big part of programming. We’ll be talking about :. Intro to R and RStudio.
rsummer.data-analysis.at
Program – R Summer School
http://rsummer.data-analysis.at/program
The focus of this course is on advanced R topics, such as:. Avoiding loops in functions. R packaging, incl. Roxygen and version control. Reproducible research with knitr, pandoc, Rmarkdown, Latex. Visualization: ggplot2 and shiny. Neural nets (deep learning) with MXNet. Webcrawling and API’s with xml2. Dplyr, purrr, readr, % %, etc.) with consistent API, functional programming and typing in R. High-performance computing and big data, with C and spark. Robust and multivariate methods in R.
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