COMPSCI 109B - Data Science 2: Advanced Topics in Data Science or return to Course Catalog Search
203546 – Section 001
|Faculty of Arts and Sciences||Computer Science||Pavlos Protopapas and Mark Glickman|
|Term||Day and Time||Location|
|Spring 2018-2019 (show academic calendar)||MW 1:30 p.m. - 2:45 p.m.||Maxwell Dworkin G115 (SEAS)|
4 (show credit conversion for other schools)
Credit in Faculty of Arts and Sciences is equivalent to:
Data Science 2 is the second half of a one-year introduction to data science. Building upon the material in Data Science 1, the course introduces advanced methods for data wrangling, data visualization, and statistical modeling and prediction. Topics include big data and database management, interactive visualizations, nonlinear statistical models, and deep learning. Part two of a two part series. The curriculum for this course builds throughout the academic year. Students are strongly encouraged to enroll in both the fall and spring course within the same academic year.
Requisite: (Must take CS 109A OR APCOMP 209A OR STAT 121A before taking CS 109B) AND (Not to be taken in addition to CS 109, OR APCOMP 209, OR APCOMP 209B, OR STAT 121, OR STAT 121B.)
Can only be taken after successful completion of CS 109a, AC 209a, Stat 121a, or equivalent. Students who have previously taken CS 109, AC 209, or Stat 121 cannot take CS 109b, AC 209b, or Stat 121b for credit.
|Eligible for cross-registration|
With permission of instructor/subject to availability
MIT students please cross register from MIT's Add/Drop application.