Advice For A Budding Data Scientist

Charles Parker riffs off of an Edsger Dijkstra note:

It’s still early days for machine learning. The bounds and guidelines about what is possible or likely are still unknown in a lot of places, and bigger projects that test more of those limitations are more likely to fail. As a fledgling data engineer, especially in the industry, it’s almost certainly the more prudent course to go for the “low-hanging fruit” — easy-to-find optimizations that have real world impact for your organization. This is the way to build trust among skeptical colleagues and also the way to figure out where those boundaries are, both for the field and for yourself.

As a personal example, I was once on a project where we worked with failure data from large machines with many components. The obvious and difficult problem was to use regression analysis to predict the time to failure for a given part. I had some success with this, but nothing that ever made it to production. However, a simple clustering analysis that grouped machines by the frequency of replacement for all parts had some lasting impact; this enabled the organization to “red flag” machines that fell into “high replacement” group where the users may have been misusing the machines and bring these users in for training.

There’s some good advice.  Also read the linked Dijkstra note; even in bullet point form, he was a brilliant guy.

Related Posts

Linear Programming in Python

Francisco Alvarez shows us an example of linear programming in Python: The first two constraints, x1 ≥ 0 and x2 ≥ 0 are called nonnegativity constraints. The other constraints are then called the main constraints. The function to be maximized (or minimized) is called the objective function. Here, the objective function is x1 + x2. Two classes of […]

Read More

Exploratory Data Analysis with inspectdf

Laura Ellis continues a dive into Exploratory Data Analysis, this time using the inspectdf package: I like this package because it’s got a lot of functionality and it’s incredibly straightforward to use. In short, it allows you to understand and visualize column types, sizes, values, value imbalance & distributions as well as correlations. Better yet, […]

Read More

Categories