moseandre
I have to be able to debug anything I work with; debugging is necessary.
>> "the neural network parsing the LIDAR did not classify the stop sign correctly, so the driving algorithm did not stop"
It's important to look into why the stop sign was mis-classified, even if the classifier is a subcomponent.
I don't know the term for expired buzzwords, though! They do turn people away.
I find some of the writing tough (i.e. impossible to read at night in bed) but the audio book for Infinite Jest narrated by Sean Pratt [1] is really nice. The narrator's speech is somehow easier to follow despite the number of clauses and whatever else in the writing.
[1] http://www.audible.com/pd/Fiction/Infinite-Jest-Audiobook/B0...
This book is autobiographical and Dyson explains his arc of passion for nuclear propulsion and Orion.
His strongest statement in this book is some deep respect for a biological scientist who, after seeing declassified army training manuals on chemical and biological warfare, supposedly discouraged the entire western hemisphere from further develomepment.
This kind of nuclear research is, thankfully, over.
95% of undergraduate statistics education is focused on formal inference. Data science, in my experience, involves a lot more exploratory data analysis [1] than formal inference (frequentist or Bayesian).
The extreme focus on inference and the hypothesis testing step in the scientific method is something people with a formal statistics education have to overcome to be productive data scientists. Or applied statisticians, really! It is more important to understand the data, organize it creatively, and find unexpected structure.
It's common to scale the elevation of a topographic map to exaggerate features [1], even non-linearly [2]. If you try to 3d print a raised relief globe without exaggeration it is surprisingly flat. If you're buying an expensive classroom globe, the specs talk about the exaggeration [2].
This is a sculpture, so whatever goes, but perhaps the designer looked at the version without exaggeration and noticed that it looked less emphatic.
I also think, as data visualization, this sculpture nicely shows the extreme change in price which occurs over small spatial distances. That's really important, and, actually, the tendency to make real estate price heatmaps with models that force continuity and prefer smoothness, like cubic splines, suppresses that aspect of the data. Railroad tracks and some beautification really are sometimes the only thing between million dollar homes and mobile parks.
It would be cool to see this with neighborhood boundaries/names on the mesh.
[1] "A raised-relief map or terrain model is a three-dimensional representation, usually of terrain, materialized as a physical artifact. When representing terrain, the vertical dimension is usually exaggerated by a factor between five and ten; this facilitates the visual recognition of terrain features." https://en.wikipedia.org/wiki/Raised-relief_map
[2] "Raised Relief: Yes. Elevations from 0-3,280 feet are magnified 60 times, higher elevations and ocean floor are exaggerated by 40 times." http://www.1worldglobes.com/1WorldGlobes/classroom_relief_gl...
You might also like this book.
A Review of Jeff Schmidt’s Disciplined Minds: A Critical Look at Salaried Professionals and the Soul-Battering System That Shapes Their Lives
http://disciplinedminds.tripod.com/business-and-society-revi... http://disciplinedminds.tripod.com/
SQL is great for choosing which columns to get out of multiple tables, somehow combined, as well as to filter which rows. If the end result is ready for R to use in modeling, that's great.
R can struggle to index and manipulate large datasets for combining/selecting columns and filtering rows, but that's the really nice stuff in SQL. They work well enough together and it's really no big deal to set both up.