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DATASCI 406 - Computational Methods in Statistics and Data Science
Fall 2022, Section 001
Instruction Mode: Section 001 is  In Person (see other Sections below)
Subject: Data Science (DATASCI)
Department: LSA Statistics
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 Details  Description  Schedule  Textbooks/Other Materials  Syllabi

Details

Credits:
3
Requirements & Distribution:
BS
Advisory Prerequisites:
[MATH 214 OR MATH 217 OR MATH 417] AND [(STATS 250 AND MATH/STATS 425) OR STATS 412 OR STATS 426] AND [STAT 306 OR EECS 183 OR ENGR 101 OR EECS 280].
BS:
This course counts toward the 60 credits of math/science required for a Bachelor of Science degree.
Repeatability:
May not be repeated for credit.

Description

This course introduces basic computational methods as needed in statistics. It is aimed primarily at undergraduate majors in Statistics and Data Science.

Selected topics in statistical computing, including basic numerical aspects, iterative statistical methods, principles of graphical analyses, simulation and Monte Carlo methods, generation of random variables, stochastic modeling, importance sampling, numerical and Monte Carlo integration.

 

Schedule

DATASCI 406 - Computational Methods in Statistics and Data Science
Schedule Listing
001 (LEC)
 In Person
28158
Closed
0
 
-
WF 8:30AM - 10:00AM1528 NUB

Textbooks/Other Materials

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Syllabi

Syllabi are available to current LSA students. IMPORTANT: These syllabi are provided to give students a general idea about the courses, as offered by LSA departments and programs in prior academic terms. The syllabi do not necessarily reflect the assignments, sequence of course materials, and/or course expectations that the faculty and departments/programs have for these same courses in the current and/or future terms.

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CourseProfile (Atlas)

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CourseProfile (Atlas)