| COURSE | TITLE | EFF YEAR | EFF TERM | DEPARTMENT | CREDIT HOURS | ||||
| MA477 | THEORY & APPL OF DATA SCIENCE | 2024 | 1 | Mathematical Sciences | 3.0 (BS=0.0, ET=0.0, MA=3.0) | ||||
| SCOPE | |||||||||
| This course builds on the foundations presented in the core probability and statistics course and the applied statistics course to develop a broad base of Advanced Data Science to some of the most common techniques in the field. The mathematical basis for each method is presented with focus on both the statistical theory and application. Topics covered may include classification and regression trees, regularization methods, splines and localized regression, and model validation. | |||||||||
|
|||||||||
| SPECIAL REQUIREMENTS: | |||||||||
| None | |||||||||
| TYPE | COURSE | EFF YEAR | EFF TERM | TRACK | RED BOOK FLG |
| PRE REQUISITE | |||||
| MA371 | 2013 | 1 | 1 | Y | |
| MA376 | 2020 | 1 | 1 | Y | |
| CY300 | 2019 | 2 | 2 | Y | |
| MA376 | 2020 | 1 | 2 | Y | |
| MA376 | 2020 | 1 | 3 | Y | |
| MA486 | 2019 | 1 | 3 | Y | |
| AYT | #SECT/SIZE | CPBLTY | ENRLD | WAIT | SEATS | CLOSED | DETAILS | ||
| 2024 - 2 | 2 | 18 | 36 | 31 | 0 | 5 | N | Hours | |
| 2024 - 8 | 1 | 18 | 18 | 0 | 0 | 18 | N | Hours | |
| 2024 - 9 | 1 | 18 | 18 | 0 | 0 | 18 | N | Hours | |
| 2025 - 2 | 5 | 18 | 90 | 60 | 0 | 30 | N | Hours | |
| 2025 - 8 | 1 | 18 | 18 | 0 | 0 | 18 | N | Hours | |
| 2025 - 9 | 1 | 18 | 18 | 0 | 0 | 18 | N | Hours | |
| 2026 - 2 | 4 | 19 | 76 | 53 | 0 | 23 | N | Hours | |
| 2027 - 2 | 1 | 18 | 18 | 3 | 0 | 15 | N | Hours | |
| COURSE | TITLE | EFF YEAR | EFF TERM | DEPARTMENT | CREDIT HOURS | ||||
| MA477 | THEORY & APPL OF DATA SCIENCE | 2022 | 2 | Mathematical Sciences | 3.0 (BS=0.0, ET=0.0, MA=3.0) | ||||
| SCOPE | |||||||||
| This course builds on the foundations presented in the core probability and statistics course and the applied statistics course to develop a broad base of Advanced Data Science to some of the most common techniques in the field. The mathematical basis for each method is presented with focus on both the statistical theory and application. Topics covered may include classification and regression trees, regularization methods, splines and localized regression, and model validation. | |||||||||
|
|||||||||
| SPECIAL REQUIREMENTS: | |||||||||
| None | |||||||||
| TYPE | COURSE | EFF YEAR | EFF TERM | TRACK | RED BOOK FLG |
| PRE REQUISITE | |||||
| MA371 | 2013 | 1 | 1 | Y | |
| MA376 | 2020 | 1 | 1 | Y | |
| MA486 | 2019 | 1 | 1 | Y | |
| CY300 | 2019 | 2 | 2 | Y | |
| MA371 | 2013 | 1 | 2 | Y | |
| MA376 | 2020 | 1 | 2 | Y | |
| COURSE | TITLE | EFF YEAR | EFF TERM | DEPARTMENT | CREDIT HOURS | ||||
| MA477 | THEORY & APPL OF DATA SCIENCE | 2020 | 2 | Mathematical Sciences | 3.0 (BS=0.0, ET=0.0, MA=3.0) | ||||
| SCOPE | |||||||||
| This course builds on the foundations presented in the core probability and statistics course and the applied statistics course to develop a broad base of Advanced Data Science to some of the most common techniques in the field. The mathematical basis for each method is presented with focus on both the statistical theory and application. Topics covered may include classification and regression trees, regularization methods, splines and localized regression, and model validation. | |||||||||
|
|||||||||
| SPECIAL REQUIREMENTS: | |||||||||
| None | |||||||||
| TYPE | COURSE | EFF YEAR | EFF TERM | TRACK | RED BOOK FLG |
| PRE REQUISITE | |||||
| MA376 | 2020 | 1 | 1 | Y | |