In the past month, I made the choice to pursue a Data Science bootcamp. It may seem that this decision has come out of nowhere, but there are many reasons that have driven me to enter into the exciting world of Data Science.
While the growing opportunities for careers for data scientists are appealing, the use of data to solve problems combined with programming has always interested me. In the past few years, I have dabbled on and off with using programming and different techniques to manipulate the data I used in my pursuits as a scientist. This pivot represents a change in my possible career path but it stays true to the interests that I have always had.
I have been interested in the pursuits of science for most of my life. From a young age, I have always been interested in the tools of science and how we can use them to understand more about the world around us. A scientific approach drives how I interact with all of the information around me.
My interests in science drove me to get a BS and MS in Geology as well as publication based on my research on seismic reconstructions of topography beneath ice sheet deposits in the Ross Sea of Antarctica. Past pursuits have already given me plenty of experiences with analyzing, manipulating and visualizing large datasets.
So why did I decide to go into Data Science in particular? For me, it is the concepts of inductive reasoning, data-based problem solving, and effective visualization of results. These concepts are also shown in another thing I love, detective shows.
Yes, detective shows or procedural crime dramas are related to my interest in pursuing Data Science. While these types of tv shows are entertaining and I have seen most of them (CSI is my favorite), my love of detective shows is tied to my use of logical reasoning.
While every episode is different, the procedure is the same. The characters (such as Jessica Fletcher pictured above) is presented with a mystery or problem. They then must examine the evidence at the scene of the crime. Additionally, more information can be gathered about the circumstances around the situation. They need to make determinations about what evidence is useful and what evidence is not as helpful. Then they have to draw conclusions based on the information they have.
For me, this is a similar workflow to Data Science. The problem, goals and challenges are always different but a data scientist must use the data they have. Then they determine if there is new data that they can gather to help with the problem. There is a determination that must be made about what information is useful to focus on in large datasets and what data is not as helpful. Then finally, they can analyze that data and use different techniques to model and visualize it to draw conclusions.
These are just my first thoughts about what I think about the field of Data Science and the use of large data sets to solve problems. Many of these concepts are new to me and I have certainly been working hard at it. However, I am enjoying the learning process and I am excited to begin my journey down this path and see where it leads me.