This is a continuation of the series of interning at tech companies in the USA. This Summer I got the great opportunity to join a great group at Knewton. Knewton is an adaptive learning company. They provide a platform on which they collect and use student course data to better recommend how best students should learn (go through a course or book). You can find out more about this process here: Knewton Platform. I was part of the analytics team below is the blog post I wrote on what I was working on at Knewton.
Ku Pima: To Measure
Trivia: Ku Pima means To Measure in the Xitsonga Language
An Analytics (Data Science) team is made up of engineers/scientists with a wide array of skills. This results from the nature of the goals the team has to meet. As an Electrical Engineering major at Wits University, I’ve spent two summers as an instrumentation engineering intern. Instrumentation deals with the task of engineering instruments that can measure certain quantities for industrial processes to be controlled. Examples of environments include manufacturing and chemical plants, houses, or even the International Space Station. I find analytics to be a similar process to instrumentation engineering in that useful measurements are sought and then the instruments to calculate those measures are engineered.
Building the Analytics Pipeline
On the Analytics team at Knewton, the data scientists develop measures that are useful to track, whether directly for a business case or for building blocks for future analytics. Within the Analytics team there is a Data Analysis component that develops analytics (measures). Another component, Data Infrastructure, engineers the pipeline (instruments) to actually calculate these analytics on a large/production scale. Initially an analytic is developed by exploring some interesting idea of a measure, using available organization data, and then refining it to arrive at the final analytic.