PAUL 450 (1ON) - Personal Finance
Personal Finance
Term: Summer 2022 - Summer Session I (05/23/2022 - 06/24/2022)
Grade Mode: Letter Grading
CRN: 70200
Start Date | End Date | Days | Time | Location |
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5/23/2022 | 6/24/2022 | Hours Arranged | ONLINE |
Start Date | End Date | Days | Time | Location |
---|---|---|---|---|
5/23/2022 | 6/24/2022 | Hours Arranged | ONLINE |
Start Date | End Date | Days | Time | Location |
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6/6/2022 | 7/29/2022 | T | 5:40pm - 8:30pm | ONLINE |
Start Date | End Date | Days | Time | Location |
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6/6/2022 | 7/29/2022 | W | 5:40pm - 8:30pm | ONLINE |
Start Date | End Date | Days | Time | Location |
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5/23/2022 | 8/12/2022 | Hours Arranged | TBA |
Start Date | End Date | Days | Time | Location |
---|---|---|---|---|
5/23/2022 | 8/12/2022 | Hours Arranged | TBA |
Start Date | End Date | Days | Time | Location |
---|---|---|---|---|
5/23/2022 | 6/24/2022 | Hours Arranged | ONLINE |
Start Date | End Date | Days | Time | Location |
---|---|---|---|---|
5/23/2022 | 6/24/2022 | TR | 5:31pm - 8:30pm | PANDRA P503 |
5/23/2022 | 6/24/2022 | Hours Arranged | ONLINE |
Start Date | End Date | Days | Time | Location |
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6/27/2022 | 7/29/2022 | TR | 5:31pm - 8:30pm | PANDRA P302 |
This course is designed to introduce and develop the financial concepts that are important to the management of public health organizations. Students will be introduced to the tools of financial management and budgeting, including utilizing financial statements, basic accounting conventions and the process of developing and managing a programmatic budget.
Start Date | End Date | Days | Time | Location |
---|---|---|---|---|
5/23/2022 | 7/15/2022 | Hours Arranged | ONLINE |
This class teaches students foundations of applied health data mining and machine learning in Python. Upon completion, students will be able to understand and apply supervised and unsupervised learning in Python to build predictive models on real world datasets. Techniques covered include (but not limited to): preprocessing, dimensionality reduction, clustering, feature engineering and model evaluation. Models covered include: generalized linear models, tree-based models, Bayesian models, support vector machines, survival analysis and neural networks. All learning objectives are achieved through active application of these techniques to real world datasets. Prereq: HDS 800 or equivalent (PHP 903, DATA 800, other stats), HDS 802 or DATA 820.
Start Date | End Date | Days | Time | Location |
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5/23/2022 | 6/24/2022 | MW | 8:10am - 10:00am | ONLINE |
5/23/2022 | 6/24/2022 | TR | 8:10am - 11:00am | ONLINE |