A required course in the Predictive Analytics Certificate Program.
Course closed to new registrations:
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Learn how to use the basics of predictive analytics and modeling data to determine which algorithms to use. Understand the similarities and differences and which options affect the models most. Discover how to verify and validate your model. Topics covered include predictive analytics algorithms for supervised learning, including decision trees, linear and logistic regression, neural networks, k-nearest neighbor, support vector machines, and model ensembles. Gain a deeper understanding of how algorithms work qualitatively by reviewing best practices and the influence of various options on predictive models. Prerequisites: I&C X425.61 Introduction to Predictive Analytics and I&C SCI X425.63 Effective Data Preparation
Prerequisites: I&C SCI X425.61 Introduction to Predictive Analytics and I&C SCI X425.63 Effective Data Preparation. See enrollment confirmation for login information.
William J. Henry, M.S., is a scientific programmer at the Navy Research Laboratory in Monterey where he regularly develops data based applications in Python. Previously, at EarthRisk Technologies, he led the development of a neural network ensemble temperature forecast model.
Dean Abbott has over 25 years of experience applying advanced data mining, data preparation, and data visualization methods in real-world data intensive problems, including fraud detection, risk modeling, text mining, response modeling, survey analysis, planned giving, and predictive toxicology. He is President of Abbott Analytics in San Diego, California and serves as chief technology officer and mentor for start-up companies focused on applying advanced analytics in their consulting practices. Mr. Abbott is a seasoned instructor, having taught a wide range of data mining tutorials and seminars for a decade.
Textbooks for your course may be purchased from any vendor or bookseller of your choice.
No textbooks are required for this course.
|Event||Date||Day||Start Time||End Time||Location||Room
|START||02/08/2016||Monday||---||---||Online (Access Begins)||---
|END||03/27/2016||Sunday||---||---||Online (Access Ends)||---