Webster Groves, MO · George Herbert Walker School of Business & Technology · Mathematics/Computer Science
- F1 2021
- Section 10
- 3 Credits
- 08/16/2021 to 10/15/2021
- Modified 08/17/2021
Tuesday, 5:30 PM to 9:30 PM, East Academic Building 210
In this course, students are introduced to predictive modeling methods, approaches, and tools. Students acquire skills in predictive analytics that will allow them to develop and use advanced predictive analytics methods. They gain expertise in the use of popular tools and software for predictive analytics and learn how to develop predictive analytics questions, identify and select the most appropriate predictive analytics methods and tools, apply these methods to answer the respective questions and preparing data-driven solutions.
CSDA 5320 Analytics Application Using Python and CSDA 5330 Data Mining
- Develop and use predictive analytics models to identify relationships
among variables and/ or for prediction.
- Use predictive analytics techniques to formulate business questions.
- Select the appropriate methods for predictive analysis, and build effective predictive models.
- Evaluate the soundness and validity of a predictive models, interpret, and report on results to a management audience.
Data Science and Predictive Analytics: Biomedical and Health Applications using R
- Author: Dinov
- Publisher: Sroinger
- Edition: 1st
- ISBN: 978-3030101879
- Ch 0. Introduction to Predictive Analytics
- Ch 6. Lazy Learning - Classification Using Nearest Neighbors
- Ch 7. Probabilistic Learning - Classification Using Naïve Bayes
- Ch 8. Divide and Conquer - Classification Using Decision Trees
- Ch 9. Forecasting Numeric Data - Regression Methods
- Ch 10. Black Box Methods - Neural Networks and Support Vector Machines
- Ch 11. Apriori Algorithm for Association Rule Learning
- Ch 12. k-Means Clustering
- Ch 13. Evaluating Model Performance
- Ch 14. Improving model Performance
- Ch 5. Dimensionality Reduction
- Ch 16. Variable/Feature Selection
- Ch 19. Text Mining and Natural Language Processing
- Final Exam
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Undergraduate Studies Catalog
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Contact Hours for this Course
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Important Technology Information
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