This past semester, I had the chance to take two courses: Statistical Machine Learning from a Probabilistic Perspective (it’s a bit of a mouthful) and Big Data Science & Capstone. In the former, we had the chance to study the breadth of various statistical machine learning algorithms and processes that have flourished in recent years. This included a number of different topics ranging from Gaussian Mixture Models to Latent Dirichlet Allocation. In the latter, our class divided into groups to work on a capstone project with one of a number of great companies or organizations. It was only a 3 credit-hour course, so it was a less intensive project than a traditional capstone course that is a student’s sole focus for an entire semester, but it was a great experience nonetheless. The Big Data science course taught us some fundamentals with big data science and normal data analysis (ETL, MapReduce, Hadoop, Weka, etc.) and then released us off into the wild blue yonder to see what we could accomplish with our various projects.
Note: As of February 2018, the repo for this website is public, so I moved the comments to the same repo instead of using a separate project for them.
This post is an embedded Github Gist that I forked. I figure that since Interview season is quickly approaching, I wanted to brush up on my skills. This gist from Github user TSiege seemed like a great start, and I have added some corrections and modifications of my own. The source of this post can be found here: The Technical Interview Cheat Sheet. Please note that there is most likely a lot of errors left to find, and surely there is a lot more that can be added. Feel free to fork either my copy or TSiege's original and improve upon our work. I'd love to collaborate with anyone who has any input!
While most definitely not the first or the last, hopefully this is a seminal moment for my own personal site. I hope to stay with seanlane.net after putting my wife through the torment of having me decide which of several domain names to settle on.