Data Science and its Applicability to the Financial Field

As an aspiring finance major, I know that data science is prevalently used in the financial industry because financial markets contain datasets that require the use of data science methods to synthesis the data. Though I understand that correlations exist, I don’t have a strong understanding these correlations, meaning following represents my speculations that may or may not exist already. According to the article, the first primary branch of data science is data analysis which seeks to provide insight on data set. This often requires an exploration of causality. Applying it to finance, I believe that data science methods can be used to determine why certain shares of stocks have dropped, even though the projected data showed that the stock would increase. Coding programs such as R or SQL can be used to determine this causality, allowing companies in the future to make better choices when investing in the stock market. Based on my prior understanding, I believe that some of this is being done in the realm of business statistics which involves using coding programs to analysis business data in order to ultimately make data driven decisions. The second branch of data science is statistics which seeks use mathematical tools and probability to draw casual claims from data that has already been collected. To this end, statistics can be used assess the probability that a given stock will increase, allowing investors to make informed decisions. Investors can do this by using statistical methods to match the data with the underlying statistical distribution of a phenomena, allowing investors to draw predictions. The final branch of data science is machine learning which seeks to develop models that make prediction on a variable. Machine learning algorithms can used to assess a potential competitor’s future investment behavior, allowing a company to make more informed decisions. Models can be developed using coding programs and ought to be constantly revised as the world continues to change.