Salutations reader! It seems I’ve started a blog to share my thoughts and interpretations of all things data science. My path to creating this blog is somewhat convoluted. It involves a high school internship, an exposure to a plethora of development and analysis tools while in college, and (perhaps unintuitively) some time working as a construction sub-contractor. (As well as various life experiences, which I will spare you those details). Alas, I’ll begin with a modest outline of the technical path that brought me here.
My exposure to object-oriented programming began relatively early, in a high school internship. I worked in a lab with an electron microscope, tasked with taking raw data and making it something intelligible. Initially I used excel spreadsheets to manipulate the data which allowed for easy importing, given this specific microscope exported directly to .csv format. However, after several roadblocks, the intractable limitations of using spreadsheets became apparent. The only reasonable step was to move to a more robust method. It was back to the drawing board, and in this case, it meant using C++ to build the program from the ground up. Having no prior experience, this meant beginning with…
std::cout<<“Hello World” ;
By the end of the internship I was able to make modest progress on the program, however, this was integral to my programming abilities and understanding the process of data analysis. After, my focus was toward the physical sciences. Throughout college my interests (and majors) varied from electrical engineering to astrophysics, finally settling on mechanical engineering. Throughout the coursework I learned the power and limitations of various tools, including C++ and Matlab. I used these tools to model physical systems, representing the results and interpretations in a comprehensive way. Dynamic system modelling and analysis is perhaps the area of study where programming skills were most frequently used. This included taking data gathered in a lab setting and modelling these systems to compare expectations with results. Further, I used the tools of mathematics and visualizations to analyze, interpret, and communicate interpretations of the data. In addition to coursework, I worked on a team that developed comprehensive and prototype-ready satellite design proposals to the air force for funding. My involvement included integrating sensory inputs with the attitude and navigation control algorithms on the GNC/ADC subsystem. My main focus was with the theory and development of Kalman filters based in Matlab.
Upon graduating, I took it upon myself to expand my programming skills. I decided to focus on Python. This was the obvious chose, as it was the most popular higher-level programming language, with a shallow learning curve, hundreds of thousands of packages and libraries, as well as countless other tools and resources.
Learning Python took the form of various online classes and challenges, as well as collaborative projects. While learning the basics, the most complex and complete project was the creation of a 2-D game based in python using primarily Pygame. I consulted with an incredible friend and colleague, Sergio Garcia, who was responsible for the lion’s share of the initial development. I was responsible for the creation and refinement of various in-game classes, certain game mechanics and physics, as well as troubleshooting bugs and developing overall game function.
Meanwhile, I was keeping up with job applications. Initially I was confident I would find a career path suiting my credentials. After hundreds of applications, a handful of callbacks, and fewer interviews, I found myself disillusioned and questioning my future as an engineer. Ultimately, the mechanical engineering field is simply saturated with graduates; and this gap grow is growing, as there are roughly ten times the graduates as there are new jobs in this field, every year.
Luckily, I was able to find a job in the construction industry, initially designing fire protection systems for commercial buildings. This role quickly grew into communicating with multiple people from various companies and projects simultaneously. It was here that I learned the importance of a cohesive team, proper coordination, and effective communication.
I did well in the construction industry, however, amid recent circumstances I realized my career path must involve working toward something with real passion. In order to stay engaged I need a field that will challenge me intellectually where careful analysis is integral to success. As it stands, the ability to program across platforms offers the modern worker versatile and marketable skills that is being applied across a diversity of industries. This prompted my search for coding bootcamps. I found many software engineering bootcamps, though I found many lack established merit and have limited course options. Luckily, a friend referred me to flatiron school. I was pleased to find their Data Science bootcamp. The curriculum covers a range of skills useful for data analysis and presentation, along with more specialized skills like machine learning and natural language processing.
I’m interested in using the tools of mathematics to find underlying trends in data, and doing the work to understand and explain to those trends. A comprehensive approach must be made given the inherit complexity in the data we do and don’t collect, or have access to. Given the incredible breadth of the industry, I have a range of interests both known and yet to be discovered. Currently, I’m developing an analysis tool to use common and advanced fitting methods on trading values of cryptocurrencies, with the intent to link world phenomena to current and upcoming trends. Still under development, it involves developing the custom mathematical tools as well as implementing a user-friendly GUI.
With the skills I develop throughout the data science bootcamp I’m looking forward to completing and refining my personal projects. This includes developing effectively with current conventions to improve functionality and legibility of the applications. Furthermore, I’ll have the tools to explore the possibilities of integrating machine learning with conventional techniques to gain more comprehensive understandings of the world’s available data. Most importantly, the tech industry is constantly evolving and applications expanding. I enjoy developing these necessary skills to be effective in the field and I look forward to the challenge of continuing to adapt and learn throughout my career.
