Integrating R and Python with Slack
Slack is all the rage these days. If you are one of the few not familiar with Slack, it is a team communication and messaging tool. Slack has raised millions of dollars and claims over 750,000 daily users. Teams from small startups all the way up to large companies use Slack. Well, data science is done best with teams, so a team communication tool should be essential for any data scientist. Currently, R and Python are among the hottest tools in data science. Thus, it would be great if those tools could interact with Slack. Lucky for you, they can.

6 Tricks I Learned From The OTTO Kaggle Challenge
1. Stacking, blending and averaging
2. Calibration
3. GridSearchCV and RandomizedSearchCV
4. XGBoost
5. Neural Networks
6. Bagging Classifier

Computational Social Science: Social Research in the Digital Age
In the last decade we have witnessed the birth and rapid growth of Wikipedia, Google, Facebook, iPhones, Wi-Fi, YouTube, Twitter, and numerous other marvels of the digital age. In addition to changing the way we live, these tools—and the technological revolution they are a part of—have fundamentally changed the way that we can learn about the social world. We can now collect data about human behavior on a scale never before possible and with tremendous granularity and precision. The ability to collect and process ‘big data’ enables researchers to address core questions in the social sciences in new ways and opens up new areas of inquiry. This course on computational social science will emphasize social science rather than computation. We will focus on how traditional concepts of research design in the social sciences can inform our understanding of new data sources, and how these new data sources might require us to update our thinking on research design.