CSC334-syllabus-Fall2016.docx - CSC 334 Advanced Data...

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CSC 334: Advanced Data Analysis Spring 2015 Wednesday 5:45PM-9:00PM Instructor Information Instructor: Dr. John McDonald Office: CST, Room 831 Office Hours: Monday, 3:30pm-5:00pm Wednesday, 3:30pm-5:00pm Phone: (312) 362-5142 Email: [email protected] Course page: Course Description The course will teach advanced statistical techniques to discover information from large sets of data. The course topics include visualization techniques to summarize and display high dimensional data, dimensional reduction techniques such as principal component analysis and factor analysis, clustering techniques for discovering patterns from large datasets, and classification techniques for decision making. The methods will be implemented using standard computer packages. Course Goals At the end of this course, the student should have a basic understanding of the following topics and be able to identify which approach is appropriate for a given data set and data analysis task to be performed: Multivariate linear regression (least-square estimation & normal equations, model building & variable selection) Principal component analysis & Factor analysis (Eigen-values and eigenvectors, scree plots, dimension reduction, factor rotation) Canonical Correlation (to assess the relationship between two sets of variables) Discriminant analysis (Fisher's discriminant function) Cluster analysis (similarity measures, hierarchical clustering & non-hierarchical clustering). Multidimensional Scaling (if time permits) Highly Recommended Books Johnson & Wichern, “Applied Multivariate Statistical Analysis”, Published by Prentice Hall, ISBN-13: 9780131877153, 2008 (6 th edition). Hair, Black, Babin, & Anderson, “Multivariate Data Analysis”, Published by Prentice Hall, ISBN- 13: 9780138132637, 2010 (7th edition). Prerequisites CSC324: Data Analysis and Regression Grading Grading in this course will be based on a combination of homework, programming and participation assignments, periodic quizzes, the midterm exam, which will be held during the 6 th week of class, and the final project. The final grade will be computed based on the following weights: Homework/programming assignments: 35%, Midterm exam on October 14 th : 30% Final project due on November 18 th : 35% The midterm exam is mandatory and you must take it to pass the course. Makeup exams/quizzes are only given in extreme circumstances (severe illness, etc.) which must be documented. Students in the undergraduate section will have a different midterm from the graduate section and the requirements for the 1 | P a g e
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homeworks will be different. Some of the graduate level material will be extra credit for the undergraduate section. There will also be different requirements for the final project.
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  • Spring '16
  • John McDonald

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