Overview
Description
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.
Requirements
Prerequisites
- Admission to the MSIS program
BADM 700
Original catalog text
Prerequisites
Admission to the MSIS program or BADM 700.
Units
Lecture3
Catalog Details
Offering
Offered: Every Spring
Terms
spring
Restrictions
Admission to the MSIS program
Attributes
Standard
Learning Outcomes
- describe the data mining methodology and identify its applications.
- describe the importance of inference and prediction and distinguish them.
- describe and distinguish between supervised and unsupervised learning methods.
- interpret model findings and write a report describing that interpretation.
- identify and describe the challenges in real-world data analytics projects.
- identify and describe “good” vs. “bad” models by virtue of evaluation metrics.
- identify a challenge/ shortcoming in each of the statistical and machine learning methods discussed in this course AND describe a solution from the current research to address/alleviate the problem.
- identify and describe a current problem that the data science community is facing currently, and describe the current corresponding research trends.