We built a visualization app to allow IOC members (and members of the general public) to explore historical Olympic results to review the success trends of various countries to determine abnormal performance trends and which countries could be future high medal achievers. All in the interest of making the olympic games more competitive and reward the countries that are developing their athletes and improving their performance in the games. (Full warning: It takes a while to load.)

The Nenana Ice Classic is an annual ice pool contest where individuals attempt to guess the exact time the Tanana River ice will break up at Nenana, AK.

The Google Foobar Challenge is Google’s secret process of hiring developers and programmers all over the world, by invitation only. Invitations are offered to individuals who search for certain programming terms in Google, or can be found as easter eggs in Google page elements. Google no longer uses the Foobar Challenge to hire developers, but this is a fun challenge for development and growth.

We developed a python package useful for chemistry for the purpose of calculating chemical formula mass in g/mol. The mole allows scientists to calculate the number of elementary entities (usually atoms or molecules) in a certain mass of a given substance. The mass of one mole of a substance is equal to that substance’s molecular weight; as for instance, the mean molecular weight of water is 18.015 atomic mass units (amu), and so one mole of water weighs 18.015 grams. This property simplifies many chemical computations. This python package will be helpful to easily calculate the chemical formula mass, convert moles to grams and vice versa, and lastly calculate the percentage mass for the atomic nature of the elements in chemistry.

How can we know whether an instant ramen tastes good or not before trying it? Will the brand matter? Does a cup noodle always taste better than a pack one? As ramen buyers, we are interested in figuring out what features are important in finding a good ramen and how we can use these features to predict whether a pack of ramen is good or bad. We used a data set with 3950 reviews from The Ramen Rater on all kinds of instant noodles to carry out the analysis.