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)
2023
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 1/25
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 0/25
Data Architecture (3c)
  • Jan P. Olausson
Seats Taken: 0/20
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
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2007