Employs Python, Excel, R, and other GUI software to explore a variety of algorithms that fall under the umbrella of predictive analytics and data mining. Learners derive meaning from data using neural networks. Learners apply statistical models including linear and logistic regression. Lastly, learners evaluate data using Naïve Bayes and Bayesian Networks. Learners demonstrate their findings via PowerPoint and short video presentations.
We offer the following class sections for this course (10-156-115). View more class specific details and registration information by selecting a class number.
Section | Status | Start/End Date | Register By | Instruction Mode | Campus | Meeting Information | Cost |
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#01 Fall | Open |
Nov 10 –
Dec 17
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Oct 25 | Online Required Due Dates | Online |
Nov 10 – Dec 17
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Online
J.J. Minarcin
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View Rates |