Data Architecture

ENGR-6220

Students design and deploy analytical systems that serve as the basis for the analysis, processing, storage, and interface of the machine learing process. Students choose learning models appropriate to the result desired using decision tree, Bayesian, neural net, and vector machine approaches. Students use multiple statistical approaches to evaluate results that lead to best results.

3 credits
Prereqs:
none

Past Term Data

Offered
Not Offered
Offered as Cross-Listing Only
No Term Data
Spring Summer Fall
(Session 1) (Session 2)
2024
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 4/25
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 0/25
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 0/25
2023
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 1/25
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 3/25
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 5/25
2022
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 6/25
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 4/25
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 3/20
2021
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 5/25
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 2/25
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 4/20
2020
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 3/20
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 3/25
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 5/20
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998