Data Science

More than ever, AI and machine learning (ML) are integral parts of our lives and are tightly coupled with the majority of the products we use on a daily basis. We use AI/ML in almost everything we can think of, from advertising to social media and just going about our daily lives! With the prevalent use of these tools and models, it is essential that, as IT systems and software became a disciplined practice in terms of development, maintainability, and reliability in the early 2000s, ML systems follow a similar trend. The field focused on developing such practices is currently loosely defined under many different titles (e.g., machine learning engineering, applied data science), but is most commonly known as MLOps, or Machine Learning Operations.

For many data science problems, there is a need to estimate unknown information from a sequence of observed events. You may want to know, for instance, whether a person is angry or happy, given a sequence of brain scans taken while playing a video game. Or you may be digitizing an ancient text, but, due to water damage, can’t tell what one word in the sequence says. Or in my case (I’m a wildlife biologist), you may want to infer whether an animal is sleeping or eating at any given moment using a sequence of animal GPS locations.

Now, there are...

Last week marked the D-Lab’s inaugural “Excel Fundamentals” workshop, and to celebrate I am sharing one of my favorite Excel functions: INDEX-MATCH-MATCH. By combining the INDEX and MATCH functions, we can create a faster and more flexible lookup than the typical approach with VLOOKUP.

First, let’s explore the INDEX function and its three arguments: INDEX(where, down, across). It returns the value of a single cell within a block of data. It knows which cell we are...