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May 10, 2024
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IS 482 - Applied Data Science (3 units)
An introduction to the most commonly used techniques in data analysis, statistical learning and machine learning. This is an applied data analytics course focusing on the theories and algorithms behind each technique from an application point of view.
Prerequisite(s): IS 350 ; Business major or minor.
Grading Basis: Graded
Units of Lecture: 3
Offered: Every Spring
Student Learning Outcomes
Upon completion of this course, students will be able to:
1. describe the data mining methodology and identify its applications.
2. describe the importance of inference and prediction and distinguish them.
3. describe and distinguish between supervised and unsupervised learning methods.
4. interpret model findings and write a report describing that interpretation.
5. identify and describe the challenges in real-world data analytics projects.
6. identify and describe “good” vs. “bad” models by virtue of evaluation metrics.
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