Introduction to Data Science In this course students will gain exposure to the entire data science pipeline: forming a statistical question, collecting and cleaning data sets, performing exploratory data analyses, identifying appropriate statistical techniques, and communicating the results, all the while leaning heavily on open source computational tools, in particular the R statistical software language. We will focus on analyzing real, messy, and large data sets, requiring the use of advanced data manipulation/wrangling and data visualization packages. Students will be required to bring alaptop (owned or college-loaned) to class as many lectures will involve in-class computational activities. (formerly MATH216) 3 hrs lect./disc.

Ivy Zhang

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It was online, so it was harder to receive feedback and further instructions from the Professor, especially about how to navigate around R. It is very project-based and you need to do 3 or 4 throughout the semester. I would recommend pairing up with someone to make the workload easier when doing the projects.