Introduction to Data Science
Introduction to Data Science (formerly MATH 0118) 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 MATH 0216) 3 hrs lect./disc.
Introduction to Data Science (formerly MATH 0118) 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 …Read more
NOTE: My professor was Professor Winder, not Malcolm-White. A very helpful and interesting course to introduce using R. Lectures move pretty fast, but Professor Winder allows your to use past activities and the internet on the activities and homework, which is very helpful. Overall, an enjoyable and very helpful class.
This class was lecture-based and then you would complete the lessons/homework in class to ensure you understood the content. The lectures were important to understanding the homeworks, which was 50% of the grade. I really recommend Professor Malcolm-White, she's super enthusiastic and answers questions extremely well.
She is a very enthusiastic teacher. The course material is not difficult at all, she is very helpful at office hours it would be easy to get a perfect score on all the homework . she is accommodating and gives extensions however grading for late work is harsh and she does not budge on that.
I love EMW. She's honestly the best! She's very accommodating and as long as you communicate with her beforehand can turn in assignments late. Half of the class was spent learning the material and the other half was spent doing homework with a group. I recommend taking this class as an intro to stats!
I really enjoyed taking this class and found it very useful. I really enjoyed the teaching style and class structure. The first half of class was a lecture and the second half we had time to work on homework, which most people were able to finish in class.
Would def take this course again. emily is great! not much homework. material is easy to find online and chat gpt is great help. ta was also super helpful. mid term was not alot of work, and final is a project...
The course is a great way to get into data science. it gives all the basics that you need and Professor Emily explains the material in a clear and easy-to-interpret way. There aren't any quizzes, only a midterm project and a final project. I recommend the class!