RedOwl Analytics

[...] for how to evaluate a candidate and how a candidate evaluates a company are still in flux. Data scientists can’t be interviewed the same way as software developers nor as marketing analysts. A [...]

[...] expect if they go, and so I’ll summarize my experience here. First of all, a LOT of data scientists do A/B testing. This makes a lot of sense for consumer web sites or apps—-Square, [...]

[...] ;what “data science” really means, illustrated by a helpful Venn Diagram:   Data scientists know the fundamentals of statistics. But compared to a statistician, they may be less [...]

[...] the law of the instrument. Below, I’ve outlined four ‘tools’ that data scientists could add to their toolbox. Although it may take you a decade to master, you can try [...]

[...] , a standardized way to manage a deployment of an application. Elastic Beanstalk can greatly reduce the amount of time it takes from getting an application running on your [...]

[...] By Ian Clark  How do you evaluate a data science team, without speaking to the team directly? Carl Anderson, Warby Parker’s Director of [...]

[...] Godsey Coming from academia, I’m a veteran of conferences, but a total n00b at “data science” events. In November, I went to a Data Science Leadership Summit in Chicago, and then [...]

[...] a little bit of everything. Drew Conway has written an excellent summary of what “data science” really means, illustrated by a helpful Venn Diagram:   Data scientists know the [...]

[...] of data? Perhaps it’s worth taking a step back before moving forward; the tenets of data analysis haven’t changed in the 35 years since John W. Tukey’s Exploratory Data Analysis ( [...]

[...] ;t enough; the ultimate goal is achieved only when usable conclusions can be drawn from the data analysis to provide real value to the end user. Thus, every big data product occupies a place in the [...]

[...] with “@compuserve.com” email accounts? I hope not. The difference between good data analysis and bad often boils down to the quality of the question. That is, (2) above should also [...]

[...] B. Version C was roundly welcomed by executives. This, of course, is yet another example of data analysis and subject matter expertise working together to be more than the sum of the parts. This [...]

[...] By Ian Clark  How do you evaluate a data science team, without speaking to the team directly? Carl Anderson, Warby Parker’s Director of Data [...]

[...] —-with voices lilting somewhere between bravado and despair. Apparently, every data science team is overstretched. It makes sense now, when I think about it: too much data, too little time, [...]

[...] the analysis and data synthesis backing the targeting information was correct. Regretfully, data analytics is often used to describe what I would actually call data regurgitation. Data regurgitation [...]

[...]   There is a history of specious analogies and aphorisms in technical literature. Software engineers in particular often embrace silliness when feeling poetic (c.f. “A monad is like a [...]

[...] a problem when (not if) a coding error has led to nonsensical results.  Finally, software engineers are the heavy equipment operators. When you’re processing massive amounts of data, [...]

[...] , data scientists (or whatever name we call them in the next decade) will be as common as software engineers and UI designers. Maybe then the supply will meet demand. A few more quick notes: Just [...]

[...] problems before they come to a head. A lot of this research can be very technical, but RedOwl data scientists put together a visual representation of some of the causes and processes behind voluntary [...]

[...] the opportunity for the government to take advantage of creative solutions developed by RedOwl data scientists and engineers to help tackle a critical national security problem. So, while I leave [...]