Data is the core of all domains from material science to healthcare.
Mastering big data requires a set of skills spanning a variety disciplines,
from distributed systems to statistics to machine learning.
This course will provide an overview of the wide area of data science,
with a particular focus on to the tools required to store, clean, manipulate,
visualize, model, and ultimately extract information from large amounts of data.
Data science is a field full of poor science and bad visualizations. We will highlight and show off some of our favorites during the semester!! Keep an eye out on the website and handouts for some fun!
Topics include (hover over for more details):
Topics include:
Throughout the entire course you will be working on a data science project which seeks to answer an interesting and important real-world question. You will be collecting your own data, cleaning it, modeling it, visualizing it, and finally presenting your results in a poster session at the end of the course. You will work in groups of four, and will be assigned a mentor TA to help you through the process.
Additionally, your project can be used as a capstone with just a few extra requirements, fully integrating what you will have learned in the course, and building a fully-functional data science application.
Check out the Final Project section under the Assignments tab to learn more!
cs1951aheadtas@lists.brown.edu
Tuesdays & Thursdays 9:00 - 10:20am
85 Waterman Street, Rm 130
Below is the grading scheme for the course:
You are given 7 late days to use throughout the semester for any assignment, excluding
the final project and labs. There is no maximum number per assignment. Details should be found
in the syllabus.
While we will try to be consistent, the syllabus is our source of truth.
If you need an extension on an assignment, please contact Ellie directly.