# 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
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| Spring | Summer | Fall
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| (Session 1) | (Session 2) |
2024 | Data Architecture (3c) Jan P. Olausson Seats Taken: 4/25 | Data Architecture (3c) Jan P. Olausson Seats Taken: 1/25 | 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
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