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COMPSCI 109A - Data Science 1: Introduction to Data Science or return to Course Catalog Search

109899 – Section 001   

SchoolDepartmentFaculty
Faculty of Arts and SciencesComputer SciencePavlos Protopapas, Kevin A. Rader and Christopher Tanner
TermDay and TimeLocation
Fall 2019-2020  (show academic calendar)MW   1:30 p.m. - 2:45 p.m.Northwest Bldg B103 (SEAS)
Credits
4  (show credit conversion for other schools)
Credit Level
Graduate and Undergraduate

Description
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.

Prerequisite(s)
Not to be taken in addition to Applied Computation 209, or Applied Computation 209A, or Statistics 121, or Statistics 121A.

Notes
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.

Exam Group
FAS09_E

 
Cross Registration
Eligible for cross-registration
With permission of instructor/subject to availability

MIT students please cross register from MIT's Add/Drop application.

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