dandermotj

You're certainly not going to study Bayesian statistics without knowing (or at least having studied) what a p-value is.

It doesn't assume the Effiecient Market hypothesis - empirical studies of returns support random returns without the imposing a model (non-parametric tests).

That's not to say returns are actually random, but in any given time range, it appears to be.

The standard evaluation versions of all dplyr functions are available, just add an underscore to the end: filter_, select_, ...

Worth the read if only for the meme of the dog at the end.

Mark Cuban is talking about it - maximal hype has been reached.

You're trying to GROUP BY on a distributed data store; your code is the problem, not Spark SQL. Use CLUSTER BY - it's distributed sibling.

Query languages like HiveQL and Spark SQL were designed to look like SQL, but they're not.

This is excruciatingly accurate.

Big four employee outsourced to big bank. The lack of in-house knowledge is always troubling. Also screen scraping data and automating processes by having a program take control of the mouse/keyboard and click/type is a big seller to banks.

The open source library simply helps a programmer put together HTTP requests. If an API crashes because it receives an unexpected HTTP request its certainly not the requesters fault.

It is about generative models - models that let us draw samples from complicated distributions.

I'm really looking forward to seeing the scientific community adopt docker as a way to distribute reproducible research and coursework.

MIT 6.S094 has a Dockerfile[^1] that contains all the software required for taking part in the class. This is a huge boon for getting stuck into the class and its coursework.

[^1]: http://selfdrivingcars.mit.edu/files/Dockerfile

Use Docker on Ubuntu on my own machine, but forced to use a Docker on Windows at work. Using Docker on Linux is an absolute joy. I totally advocate it. Dockerhub is my first port of call when I'm installing something new on my machine because I know if there's an official repo it will work.

Docker outside of Linux is another story. I've run into config issues, VM memory problems, proxy issues, ect. Painful.

The references made in the article are probably the best place to start.

There's no such thing as reliably predicting a crash - that's why it's called a crash. There's bulls and bears but no one side is 'right' in aggregate.

It's not a replacement for TeX. R originally had Sweave for weaving R and text together. Then we got Rmarkdown, which introduced the use of markdown for authoring documents via pandoc, but the output document only had simple features like headers, sub headers, emphasis and so on.

Now Yuhui from Rstudio (who's been prolific in this area) has written bookdown for authoring books with rmarkdown. It's not for authoring statistical papers (as the other comments stated - that's rmarkdown's realm) it's for books like Hadley Wickham's R4DS and Advanced R. Other examples can be found on the bookdown website.

I'd also add that, regardless of inserting R code, bookdown is a class authoring format and personally I would write a book or large document in bookdown even if it was just pure prose.

If my understanding is correct, the perturbations are inherent in the model, not the data. It's a vulnerability in the high dimensional decision boundary of n nets.

It depends on what the variance of the distribution is and how skeweded it is.

For those who are interested in the actual implementation of these models check out simmer in R, SimPy in Python or SimJulia in Julia. Simmer was just released recently and has excellent examples/tutorials [0] for an introduction.

[0] http://r-simmer.org/articles/

A contradiction - data science is the place where statistics and computer science meet but this book definitely isn't characterised as a book about computer science and graphs. From the TOCs, it is heavily based in statistics.

Table of Contents:

* High Dimensional Space

* Best Fit Subspace & SVD

* Random Walks & Markov Chains

* Machine Learning

* Massive Data: Streaming, Sketching, Sampling

* Clustering

* Topic Models, Hidden Markov Process, Graphical Models, and Belief Propagation

Does anyone know if this is available as a hard copy?

You're not wrong and absolutely totally wrong at the same time. R is the furthest thing from C you could find in paradigm, syntax and performance, but yes much of the underlying code is C or Fortran.

But really you're missing the point. R's purpose is interactive, exploratory and scientific computing and that's what it is incredibly good at. It wasn't intended for high performance computing, but there are ways of getting it there. Look out for Rho in the future.

This is awesome - been waiting for it for ages! But bindings to the python API and not C++?

I saw the vctrs package repo the other day on your GitHub. What's your plan with that? I guess your covering all of R's base data types (dplyr:data frames, purrr:lists, forcats:factors, vctrs:vectors)? Also do you plan on developing further functional programming packages?

I read the opening two pages but I'm not sure what this really is. A higher math text book? It looks great though!...

It looks exactly like a population heatmap.

Or Rmarkdown

That was the joke I was making...

The "big data vs. small data" argument isn't the issue for data scientists. It's the total lack of decent statistical libraries, visualisation tools, programming capabilities and on and on. Don't think anything I've worked on would come close to hitting the excel memory ceiling, but without a doubt completing it in excel would be nigh on impossible.