The + Stands for
In the mathematical proof technique of induction, a theorem is proven when a property holds for a base case (n) and an arbitrary next case (n + 1). That’s where Enplus gets its name.
We approach challenges step by step from introduction through resolution. Each step is incremental, but it forms the basis for the next step in solving highly complex problems. We also always take one more step at the end to make sure we deliver top-shelf instruction to your team.
We Think of
At Enplus, every solution we provide is based on your business objectives. We start every interaction by listening to your team to be sure that our training helps you achieve your goals. You can expect us to be honest. We’ll go to work on customizing an instructional plan that is right for your company.
Wrote the Book
Enplus Advisors founder Dan Gerlanc leverages a decade in quantitative data analysis and 15 years in custom software writing to create our unique perspective centered on the connection between data science and software. (He’s an in-demand data science expert.)
Dan works with a team of the best brains in the business, assembling the right group of instructors to deliver instruction at a level of quality that exceeds your expectations. These exceptional professionals deliver hard-won learnings that your team can apply to their work for years to come.
What Our Clients Say
"By myself, I could only clean up data from about 100 publicly traded companies and I still made substantial errors. With [Enplus], there’s no lapse in data and we’re now analyzing 2,600 stocks. That’s a 260 percent increase in coverage."
— Financial Analysis Company
"Enplus delivered high-quality code. Their deliverables are ready to adapt to any changes we need in the future. This level of flexibility is important."
— Biotechnology Company
"I’d worked with some other firms before and didn’t feel like they were going the extra mile to understand what I wanted. Enplus does—and I get the impression there’s a lot of double- and triple-checking along the way to make sure the output is as good as it can be. This is especially valuable when we know the input data is kind of suspect."