This past spring my laptop of 5 years died and had to be replaced. Unlike the more forward thinking experts out there, I failed to hang on to most any installation scripts, so it’s taken some time to re-build my local work environment to my liking.
I’ve made a commitment to organizing much more on the Windows Subsystem for Linux (Ubuntu 22.04 jammy jellyfish) instead of Windows this time for speed and code interoperability.
Cycling 🚲 popularity has grown in 2021 as evidenced by local cycling club activity and US national surveys. Some of this may be related to changes like new e-bike configurations and city-wide bike share systems that make it accessible to more people. It’s also possible that post-COVID, many people still want fun & affordable exercise.
Bike shops have been busy filling orders and making repairs. Lead times for some models are months long as global supply chains work to catch up.
More often than not, the most impactful analytics projects do not involve any artificial intelligence. The real financial return comes from insights delivered to the organization via Exploratory Data Analysis, or EDA.
Analytics professionals who focus on decision science, that is, people that use data to provide ground truth to the business, must be able to articulate what the EDA process is and what the standards are for the process. Analysts should be the best people available to find meaning in the data.
I was inspired by David Robinson’s Tidy Tuesday screencast to work with rtweet on familiar Twitter handles. Writing on rtweet is an amusing bit of productive procrastination while honing my R skills.
The rtweet package creator and owner, Mike Kearney, has hosted data journalism workshops covering a wide range of uses for this kind of data. They include text sentiment, geospatial networks, time series, and even bot detection. Oscar Baruffa and Veerle van Son have written a bookdown pamphlet called Twitter for R Programmers as a starter guide for people new to the R community on Twitter.
Signals are everywhere around the world today. Audio communications are more critical than ever. Airplanes use signals in the air to obtain important information to ensure safety on a flight. Cell phones are pretty much a single small high performance digital signal processing device. They process our speech when we talk by removing background noise and echos that would distort the clarity. They obtain wifi signals to allow for web searches.
The entry point for most of us into analytics is our experience with Microsoft Excel. At some point though, projects and organizations require more. The breakdowns are typically:
We need to separate the data from the analysis, with storage, for a single source of truth.
We need to be able to automate the analysis.
We need the analysis to be reproducible.
We should not pay a third party obscene amounts of money for something as basic as arithmetic.
This week’s #TidyTuesday subject is a study of 342 observations of Antarctic penguins on the Palmer Archipelago.
Our goal here is first to predict the sex feature missing from several of the penguins in the dataset using a classification model, driven by what is known in other observations in the dataset.
Then, we will take a closer look at an expert data visualization crafted by Cedric Scherer.
And finally, we will close with a discussion of several methods of assessing variable importance.