APCOMP 209A - Data Science 1: Introduction to Data Science or return to Course Catalog Search
109898 – Section 001
|Faculty of Arts and Sciences||Applied Computation||Pavlos Protopapas, Kevin A. Rader, Margo Levine and Rahul Dave|
|Term||Day and Time||Location|
|Fall 2017-2018 (show academic calendar)||MW 1:00 p.m. - 2:29 p.m.||Northwest Bldg B103 (SEAS)|
4 (show credit conversion for other schools)
Credit in Faculty of Arts and Sciences is equivalent to:
Data Science 1 is the first half of a one-year introduction to data science. The course will focus on the analysis of messy, real life data to perform predictions using statistical and machine learning methods. Material covered will integrate the five key facets of an investigation using data: (1) data collection - data wrangling, cleaning, and sampling to get a suitable data set; (2) data management - accessing data quickly and reliably; (3) exploratory data analysis ? generating hypotheses and building intuition; (4) prediction or statistical learning; and (5) communication ? summarizing results through visualization, stories, and interpretable summaries. Part one 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. Part one of a two part series.
Not to be taken in addition to Computer Science 109, or Computer Science 109A, or Statistics 121, or Statistics 121A.
Only one of CS 109a, AC 209a, or Stat 121a can be taken for credit. Students who have previously taken CS 109, AC 209, or Stat 121 cannot take CS 109a, AC 209a, or Stat 121a for credit.
|Eligible for cross-registration|
With permission of instructor/subject to availability
MIT students please cross register from MIT's Add/Drop application.