MSDA3051
APPL'D NATURAL LANG PROC (NLP)
- Department
- Master of Sci in Data Analytic
- Terms offered
- 2
- Data refreshed
- Jun 27, 2026
Description
Natural Language Processing (NLP) is a rapidly advancing domain, encompassing areas such as the hard sciences, humanities, and social sciences. Its applications are highly regarded across academia, government, and industries. This course delves into the intricacies of NLP algorithms, emphasizing their real-world application. A significant portion of the topics will be dedicated to understanding both generative and non-generative models in NLP, exploring their strengths, weaknesses, and use-cases. Students will gain insights into how these models' function, their underlying mechanisms, and their practical implications in natural language tasks. The course sheds light on the core issues and resolutions in NLP, illustrating their connections with linguistics and statistics. It provides a comprehensive insight into the methodologies used by computers to interpret and produce human language, integrating key principles from Applied Machine Learning and Data Engineering.
Syllabi
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Offering history & forecast 2024–2025
| Year | Fall | Spring | Summer | Intersession |
|---|---|---|---|---|
| 2030 | 2030 Fall: 20% chance, low confidence. Offered in 1 of 3 fall terms since 2024. | 2030 Spring: not enough history to forecast. | 2030 Summer: 7% chance, medium confidence. Offered in 0 of 9 summer terms since 2018. | 2030 Intersession: 8% chance, medium confidence. Offered in 0 of 6 intersession terms since 2020. |
| 2029 | 2029 Fall: 20% chance, low confidence. Offered in 1 of 3 fall terms since 2024. Department course count rising about 3% per year. Department faculty is about 24% below its recent average. | 2029 Spring: not enough history to forecast. | 2029 Summer: 6% chance, medium confidence. Offered in 0 of 10 summer terms since 2017. | 2029 Intersession: 7% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2028 | 2028 Fall: 21% chance, low confidence. Offered in 1 of 3 fall terms since 2024. Department course count rising about 5% per year. Department faculty is about 21% below its recent average. | 2028 Spring: not enough history to forecast. | 2028 Summer: 5% chance, medium confidence. Offered in 0 of 11 summer terms since 2016. | 2028 Intersession: 7% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2027 | 2027 Fall: 22% chance, low confidence. Offered in 1 of 3 fall terms since 2024. Department course count rising about 5% per year. Department faculty is about 21% below its recent average. | 2027 Spring: not enough history to forecast. | 2027 Summer: 5% chance, high confidence. Offered in 0 of 12 summer terms since 2015. | 2027 Intersession: 6% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2025 | Not offered | 1 section | Not offered | Not offered |
| 2024 | 1 section | Not offered | Not offered | Not offered |
Future estimates combine recent offering frequency with detected multi-year cycles. Hover a cell for confidence and evidence; low-confidence estimates are marked with ~.
Meeting times
Most recent first- Fri, 9:00 AM–11:50 AM ONLINE 2025
| Term | Professor | Type | Credits | Enrollment | CRN |
|---|---|---|---|---|---|
| Spring 2025 | Fatema B. Nafa | Online | 1 | 5 of 30 enrolled | 34017 |
| Fall 2024 | Fatema B. Nafa | Online | 1 | 9 of 30 enrolled | 23354 |