Data Science Takeaways
What I would be doing differently:
Data science is a huge field and, with the help of ST558: Data Science for Statisticians course offered by the math department at North Carolina State University, I learned a lot about about different prospects of it. After going through coursework throughout the semester, I am confident to say that from now on all of my visualizations will be done on R language since it offers a broad range of plots, methods of visualizations, and then some. The ease of plots and representations surpasses any other languages, and during the course, I got to know about a lot of them.
With the help of packages such as tidyverse, I also am confident that querying through the data, manupulating it, will all be done on R language, and, to build quick machine learning models and to see model’s predictions, I would be using caret package to do so, rather than doing it so on any other language.
In professional career or during my degree, if I would have to present my findings, I will much rather create a interactive Shiny app, than any other forms of representation since it can be deployed anyehere, making it easier and unique in audience’s perspective.
Another point I would like to note out is the leaflet package, even though we did not have any detailed conversations about it in the class, I used it extensively for two of my other courses that required geo-spatial analysis, and I am a big fan of that, hence leaflet package is something I will keep handy to do any geo-spatial analysis.
Future Career
I am looking for a career in Data Science/Operations Research field itself. With my previous proferssional work experience, and with the summer internship’s project, I know a few applications of Operations Research and Data Science, and I would like to apply my knowledge in the same area. Specifically, I am looking for a career in geospatial analysis, like weather science, or environmental science, or something like what I did in my 2nd project. Hence naturally, I would like to learn more about packages that enables these types of analysis, maybe leaflet for example.
In addition to that, I would also like to know details and more applications of each type of machine learning models that we learned in the class, and where in the real world applications are they actually implemented. This would make a few things a lot clearer.