Summer2022

I have a good idea of what I think I want to do.

A clearer Focus

I have been focusing on Political Science as my major however, I have been more affiliated with R programming and data analysis. I was not able to follow with my IST career when I transferred here though I was told it would be a possibility when I decided to transfer. My decision to transfer will be covered in another post or perhaps not. I don’t know at this time. I am really just getting back into the swing of things with my new laptop and getting my keybinds and settings the way I want. Now running Arch as my daily driver I have been saying “I run arch by the way” more often but I also have been messing with the suckless way of doing things.

Learning R

Anyways to get into an overview of things I have been learning about base R and the tidy data package. I’m going to be honest, tidy data is not fun. Base R however with the metrics package is cool though. In one class I learned a lot that could be described as the basis for machine learning.That ended with creating models that had no predictors and just used the data for statistical inference. Topics covered were, linear regression, cross-validation, Principal Components Analysis, Classification Methods, Parametric Methods and K-Nearest Neighbors, Support Vector Machines and Clustering Methods, and Tree-Based Methods and XGboost. The teachers overview is as follows.

This is a case study-based course in the use of computing and statistical reasoning to answer data-intensive questions. This course addresses the fact that real data are often messy by taking a holistic view of statistical analysis to answer questions of interest. Various case studies will lead students from the computationally intensive process of obtaining and cleaning data, through exploratory techniques, and finally to rudimentary inferential statistics. This process will exploit students’ exposure to introductory statistics as well as the R programming language, hence the required prerequisites, yet novel computing and analytical techniques will also be introduced throughout the course. For the collection of data, students will learn scripting and database querying skills; for their exploration, they will employ R capabilities for graphical and summary statistics; and for their analysis, they will build upon the basic concepts obtained in their introductory statistics course. The varied case studies will elucidate additional statistical topics such as identifying sources of bias and searching for high-dimensional outliers.

Conclusion

I plan on just continuing to learn R and better understand data analysis with my degree. While I continue to work on my server and build up my knowledge of mangaing a server. And everything that would be involved in a IT support/ analysts role.