Getting into a technical field like data science is really difficult when you're fresh out of school. On the off-chance that your potential employer actually gets the hiring process right, most organizations are still going to place a considerable amount of weight on experience over schooling. Like, yeah there are certain schools that make it a lot easier to go from academia to industry, but otherwise you're dealing with the classic catch-22 situation.

Something that can help you – and what I would notice when reviewing applications – is having something original and interesting (even if just to you) to show and talk about. It doesn't have to be published original research. It doesn't have to be a thesis. It just has to show that you can:

• Work with real data: In most academic programs, methods are taught using clean, ready-to-use data. So it's important to show that you can take some data you found somewhere and process into something that you can glean insights from. It also gives you a chance to work with data about a topic that you personally find interesting. Possible sources of data include:
• Explore it: Once you have a dataset that actually excites you, you should perform some EDA. Produce at least one (thoroughly labeled) visualization that shows some interesting pattern or relationship. I want to see your curiosity. I want to see an understanding that you can't just jump into model-fitting without developing some familiarity with your data first.
• Analyze it: You're going to lose a lot of interest if you just show and talk about how you followed the steps of some tutorial verbatim. If you learn from the tutorial and then apply that methodology to a different dataset, that's basically what "experience" means. And don't try to use an overly complicated algorithm/model if the goal doesn't require it. You might get incredible accuracy classifying with deep learning, but you'll probably have a more interesting story to tell from inference with a logistic regression. Heck, at Wikimedia we use that in our anti-harassment research.
• Present your work: It can be a neat report with an executive summary (abstract) or it can be an interactive visualization or a slide deck. Just something better than zip archive of scripts or Jupyter notebooks.
• Explain your work (however complex) and results in a way that can be understood: This is where the first point is really important. If you're describing your analysis of data from a topic you're familiar with and are interested in, you're going to have a much easier time explaining it to a stranger. Be prepared to talk about it to a non-technical person. Be prepared to talk about it to a technical person who may not be familiar with your particular methodology. Your interviewer may have done a lot of computational lingustics & NLP but no survival analysis, so get ready to give a brief lesson on K-M curves (and vice versa).
• Perform an analysis from start to finish: Because that's what we look for when we assign a take-home task to our candidates.

A lot of times the job postings will include a number of years as a requirement, but that's not as need-to-have as you or they might think. Secretely, it's actually a nice-to-have because "experience" is mostly a proxy for "candidate has previously used real data to solve a problem in a way that can be understood and used to inform a decision-making process." If you don't have experience, you can still demonstrate that you've done what a data scientist does.

Good luck~

Acknowledgement: I would like to thank Angela Bassa (Director of Data Science at iRobot) for her input on this post. In particular, the last paragraph is based entirely on her suggestions. She also created the Data Helpers website that lists data professionals who are able to answer questions, promote, or mentor newcomers into the field.

# Installing GPU version of TensorFlow™ for use in R on Windows

The other night I got TensorFlow™ (TF) and Keras-based text classifier in R to successfully run on my gaming PC that has Windows 10 and an NVIDIA GeForce GTX 980 graphics card, so I figured I'd write up a full walkthrough, since I had to make minor detours and the official instructions assume -- in my opinion -- a certain level of knowledge that might make the process inaccessible to some folks.

Why would you want to install and use the GPU version of TF? "TensorFlow programs typically run significantly faster on a GPU than on a CPU." Graphics processing units (GPUs) are typically used to render 3D graphics for video games. As a result of the race for real-time rendering of more and more realistic-looking scenes, they have gotten really good at performing vector/matrix operations and linear algebra. While CPUs are still better for general purpose computing and there is some overhead in transferring data to/from the GPU's memory, GPUs are a more powerful resource for performing those particular calculations.

Notes: For installing on Ubuntu, you can follow RStudio's instructions. If you're interested in a Python-only (sans R) installation on Linux, follow NVIDIA's instructions.

# Prerequisites

• An NVIDIA GPU with CUDA Compute Capability 3.0 or higher. Check your GPU's compute capability here. For more details, refer to Requirements to run TensorFlow with GPU support.
• For example, I like using Microsoft R Open (MRO) on my gaming PC with a multi-core CPU because MRO includes and links to the multi-threaded Intel Math Kernel Library (MKL), which parallelizes vector/matrix operations.
• I also recommend installing and using the RStudio IDE.
• You will need devtools: install.packages("devtools", repos = c(CRAN = "https://cran.rstudio.com"))
• Python 3.5 (required for TF at the time of writing) via Anaconda (recommended):
1. Install Anaconda3 (in my case it was Anaconda3 4.4.0), which will install Python 3.6 (at the time of writing) but we'll take care of that.
2. Add Anaconda3 and Anaconda3/Scripts to your PATH environment variable so that python.exe and pip.exe could be found, in case you did not check that option during the installation process. (See these instructions for how to do that.)
3. Install Python 3.5 by opening up the Anaconda Prompt (look for it in the Anaconda folder in the Start menu) and running conda install python=3.5
4. Verify by running python --version

# Setting Up

## CUDA & cuDNN

1. Presumably you've got the latest NVIDIA drivers.
2. Install CUDA Toolkit 8.0 (or later).
3. Download and extract CUDA Deep Neural Network library (cuDNN) v5.1 (specifically), which requires signing up for a free NVIDIA Developer account.
4. Add the path to the bin directory (where the DLL is) to the PATH system environment variable. (See these instructions for how to do that.) For example, mine is C:\cudnn-8.0\bin

## TF & Keras in R

Once you've got R, Python 3.5, CUDA, and cuDNN installed and configured:

1. You may need to install the dev version of the processx package: devtools::install_github("r-lib/processx") because everything installed OK for me originally but when I ran devtools::update_packages() it gave me an error about processx missing, so I'm including this optional step.
2. Install reticulate package for interfacing with Python in R: devtools::install_github("rstudio/reticulate")
3. Install tensorflow package: devtools::install_github("rstudio/tensorflow")
4. Install GPU version of TF (see this page for more details):
library(tensorflow)
install_tensorflow(gpu = TRUE)
5. Verify by running:
use_condaenv("r-tensorflow")
sess = tf$Session() hello <- tf$constant('Hello, TensorFlow!')
sess\$run(hello)
6. Install keras package: devtools::install_github("rstudio/keras")

You should be able to run RStudio's examples now.

Hope this helps! :D

# Yo, NieR: Automata is super awesome

This weekend I got super into a new videogame called NieR: Automata (available on PS4 and PC). I saw a bunch of folks tweeting nothing but praise about it, so I decided to check out the demo on PSN. I was so blown away by it that I actually got into my car, drove to the nearest GameStop, and picked up a copy. I cannot remember the last time a game demo did that to me, if ever. This game is ⚡️E⚡️X⚡️T⚡️R⚡️E⚡️M⚡️E⚡️L⚡️Y⚡️ 💥 ⚡️G⚡️O⚡️O⚡️D⚡️, and I highly recommend it if you're into games like DmC: Devil May Cry and other PlatinumGames titles.

It borrows so many ideas from so many games and genres, but the outcome doesn't feel like a Frankenstein's monster. It all feels cohesive.

The little touches in this game are really endearing. Like when 2B gets off a ladder and does a flip onto a platform, or when she occasionally slides down the side of a ladder. The animations feel at once both completely superfluous but also absolutely necessary.

NieR: Automata is a game that I'm glad to not be reviewing, because I would be staring at an empty document, thinking, "They should have sent a poet."[1]

# Probabilistic programming languages for statistical inference

This post was inspired by a question about JAGS vs BUGS vs Stan:

Explaining the differences would be too much for Twitter, so I'm just gonna give a quick explanation here.

# BUGS (Bayesian inference Using Gibbs Sampling)

I was taught to do Bayesian stats using WinBUGS, which is now a very outdated (but stable) piece of software for Windows. There's also OpenBUGS, an open source version that can run on Macs and Linux PCs. Benefits include: academic papers and textbooks written in 80s, 90s, and early 2000s that use Bayesian stats might include models written in BUGS. For example, Bayesian Data Analysis (1st and 2nd editions) and Data Analysis Using Regression and Multilevel/Hierarchical Models use BUGS.

# JAGS (Just Another Gibbs Sampler)

JAGS, like OpenBUGS, is available across multiple different platforms. The language it uses is basically BUGS, but with a few minor differences that require you to rewrite BUGS models to JAGS before you can run them.

I used JAGS during my time at University of Pittsburgh's neuropsych research program because we used Macs, I liked that JAGS was written from scratch, and I preferred the R interface to JAGS over the R interfaces to WinBUGS/OpenBUGS.

# Stan

Stan is a newcomer and it's pretty awesome. It has a bunch of interfaces to modern data analysis tools. The language syntax was designed from scratch by people who wrote BUGS programs and thought it could be better and were inspired by R's vectorized functions. It's strict about the type of data (integer vs real number) and about parameters vs transformed parameters, which might make it harder to get into than BUGS which gives you a lot of leeway (kind of like R does), but I personally like constraints and precision since that's what allows it to be hella fast. Stan is fast because it compiles your Stan models into C++ (hence the need for strictness). I also really like Stan's Shiny app for exploring the posterior samples, which also supports MCMC output from JAGS and others.

The latest (3rd) edition of Bayesian Data Analysis has examples in Stan and Statistical Rethinking uses R and Stan, so if you're using modern textbooks to learn Bayesian statistics, you're more likely to find examples in Stan.

There are two pretty cool R interfaces to Stan that make it easier to specify your models. The first one is rethinking (accompanies the Statistical Rethinking book I linked to earlier) and then there's brms, which uses a formula syntax similar to lme4.

Stan has an active discussion board and development, so if you run into issues with a particular model or distribution, or if you're trying to do something that Stan doesn't support, you can reach out there and you'll receive help and maybe they'll even add support for whatever it is that you were trying to do.