Most good questions start over a round of beers and this is no different. During game 5 of the 2014-2015 NBA finals my friends and I got into a typical sports argument; who is the best and why? I had been arguing that Lebron James' performance in these finals was historically good but my friends disagreed. Forcefully. As this discussion flowed and ebbed we got into rating styles of professional basketball itself, and eventually, what makes a good NBA draft. Let's take a look using data from basketball-reference.com.

# A Visit to a High School Calculus Class

It's funny what high school students notice. I paid a visit to a high school calculus class and gave two lectures about applied math. Both lectures were variants of a typical themes; *what is mathematics and how is it used* in today's society. The talk was very visual and used two mathematical ideas, network theory and optimization, to motivate the study and beauty of mathematics.

I began the talk by asking them to consider the following picture from a children's book. I borrowed this from a talk given by Timothy Gowers on "The Importance of Mathematics." The students very quickly realize the water spouting from the lazy elephant is not obeying Newton's law of gravity. From here I go into a short bit on mathematical modeling. I ask them what they know about bacteria and I receive a typical answer; bacteria grows exponentially. We look at the graph and they realize, through a verbal discussion, that . From here the students understand the exponential growth model needs to be refined. We end with the graph of the logistic equation.

# A WhatsApp Conversation Visualized, or, Textual Healing

Nothing says romance like analyzing WhatsApp text messages using Python. In an effort to understand my relationship with my girlfriend I present, with lovely graphs, our texting habits.

Using Python and some of its standard packages like Numpy, Scipy, Matplotlib, and Pandas, I parsed data from our WhatsApp conversations. The data began on August 12, 2014, at 10:47pm local time in Amsterdam. The last entry used is from May 18, 22:45 Mountain time. There are a total of 27192 texts. The first table gives some statistics regarding the data.