Corporate conferences like Google I/O and WWDC get all the hype, but the best technical talks usually happen at smaller venues. In February, Stanford hosted a series of technical talks by prominent data scientists. The event had little fanfare, but the talks are a must-watch.

Data science is a broad discipline, joined by one common goal: use data to solve interesting problems. Data scientists' careers depend on whether they can find those interesting problems. The Stanford workshop brings together successful data scientists working on topics that range from human rights to enterprise software. The resulting talks are case studies for how data science can be applied in a variety of fields.

Thankfully, Stanford posted all of the talks online. Go watch all of them! If you don't have time for all of them, we recommend checking out the three below.

Data science for human rights

Megan Price works for the Human Rights Data Analysis Group, a non-profit organization that acts as "behind-the-scenes data scientists for human rights advocates." They perform statistical analyses of large scale violence and human rights violations. She speaks about how she estimated the number of deaths caused by the ongoing Syrian conflict. This problem is technically challenging because deaths are reported repeatedly and imperfectly. It also has great social significance in helping the world understand the impact of the Syrian civil war.

Optimizing HIV treatment

Finale Doshi-Velez is a professor at Harvard University, where she applies data science to clinical healthcare problems. Her talk focuses on finding the best treatment plan for HIV patients. Creating a treatment plan for an HIV patient is challenging because the disease rapidly mutates, becoming resistant to certain drugs. Drugs need to be applied in an order that minimizes the likelihood that the patient will become resistant to too many drugs too soon.

Visualizing data

Miriah Meyer is a professor at the University of Utah, where she uses data visualization to help other researchers understand complex data sets. In her talk, she explains how data visualization isn't about making things pretty: it's about presenting data in such a way that a researcher can glean new insights about the data. She provides examples, starting with a story about trying to compare two animals' genomes. This talk is an engaging introduction to the science behind data visualization.