QMSS GR5058

The class is roughly divided into two parts: 1. programming best practices, exploratory data analysis (EDA), and unsupervised learning 2. supervised learning including regression and classification methods In the first part of the course we will focus writing R programs in the context of simulations, data wrangling, and EDA. Unsupervised learning is focused on problems where the outcome variable is not known and the goal of the analysis is to find hidden structure in data such as different market segments from buying patterns or human population structure from genetic data. Supervised learning deals with prediction problems where the outcome variable is known such as predicting the price of a house in a certain neighborhood or an outcome of a congressional race.

Introduction to R (dplyr, ggplot2), natural language processing (text and sentiment analysis), unsupervised learning, and supervised learning (linear regression, classification, non-linear models, deep learning and neural networks)

Learned supervised learning concepts, unsupervised How to wrangle and analyze data in R including modern best practices Everything is done as closely as possible in a social science context Social considerations in ML/AI (ethics)

The course starts with an introduction to R, which seems well paced initially. However, post midterm, the class begins to get steeply uphill, with a new topic introduced almost every week. That doesn't allow students an opportunity to really understand a topic. Generally, it feels like the course is too broad, but with very little depth in any topic. The workload and assignments are nice, but the exams are harsh.

Data Mining: R (dplyr + ggplot2), _some_ linear algebra, how to google your way around questions and issues, some unsupervised and supervised learning.

lots of R. how to code in R. how to do things in R. Some linear algebra for regression. Some analysis techniques. supervised learning. a lot of data wrangling skills.

I learned so much in this course. I came in with zero R knowledge and walk out feeling fairly competent in the language (major packages we learned: tidyverse, tidytext, tidymodels, ggplot2). We also learned the basics of linear algebra and matrix multiplication as well as machine learning methods like random forest, elastic net regression, and more. This course taught me the most this semester.

If you are not taking any other super time consuming courses, then I would probably recommend. This assignments for this course will take up a majority of your time.

More time spent on the 2nd half of the class instead of R

Although this is a very interesting course, the course content is too condensed (or at least, highly unbalanced between the first and second half of the semester).