Insider trading is something that we’ve heard about time and again in the news. It made headlines back in 2001 when Enron executives were charged with insider trading and accounting fraud. It has been a significant challenge for the financial regulatory body of the U.S government, the SEC, to both prevent and identify. I became fixated on applying data mining techniques to aid financial regulators in identifying and preventing illegal insider trading. I feel that using recent advances in big data to make the market safer and fairer, is both a noble and important pursuit. I know that I want to pursue a career as a data scientist, and being able to explore and invent techniques to solve problems with big data is crucial. It is also important to my future career goals that I am able to use my technical background and skill set to solve problems of vastly different types, in different domains. My work this summer is a continuation of the work I have been doing since September of last year. I am working with insider transaction data from about 400 different companies in different sectors. Each week I make it a goal to read a paper related to my project. I write new scripts to perform different analysis on my datasets; and will often experiment with different mathematical techniques to summarize and describe new trends and correlations I observe. I spend a fair amount of time using different python packages to both build creative plots and figures, and summarize my results concisely. I find that being able to explain your results clearly is impossible without the use of a good graphing tool. Since my project began, I have discovered how information propagates between insiders, how pricing data on stock is affected by financial news, and how certain network topologies are useful for representing relationships between insiders. The most important resources I have discovered are the faculty and graduate students who have taught me how to form a hypothesis and conduct research.