|Select the Course Number to get further detail on the course. Select the desired Schedule Type to find available classes for the course. The Schedule Type links will be available only when the schedule of classes is available for the selected term.|
|CS 50025 - Foundations Of Decision Making|
Credit Hours: 1.00. This course provides an overview of data science methods used for data-driven discovery, extraction of knowledge, and informed decision making. The course covers fundamental computational methods and statistical techniques used to correctly reason about uncertainty, conduct hypothesis tests, infer causal relationships, and apply and evaluate predictive models. The course highlights how sampling biases can impact fairness in decision making. Throughout, students get hands-on experience on how to make correct and explainable inferences from data. Experience in Python Programming, Probability, Statistics and Linear Algebra is required.
1.000 Credit hours
Levels: Graduate, Professional, Undergraduate
Schedule Types: Distance Learning
Offered By: College of Science
Department: Computer Science
May be offered at any of the following campuses:
Learning Outcomes: 1. Support and justify claims with evidence from data and discuss how human biases can impact interpretations. 2. Recognize the basic approach to formulating statistical hypothesis tests and apply parametric hypothesis tests for both discrete and continuous data. 3. Interpret p-values and apply them effectively to support claims about data. 4. Formulate and test conjectures with controlled experiments, including A/B testing. 5. Recognize the types of errors encountered when conducting multiple hypothesis tests. 6. Use computational approaches based on randomization for hypothesis testing and uncertainty quantification. 7. Discuss ethical issues such as privacy and fairness that can be associated with automating decisions based on machine learning methods. 8. Apply the basic components of classification methods using Python libraries. 9. Evaluate the performance of learned models with learning curves and k-fold cross validation and assess significance with hypothesis tests.