Challenge submission deadline:
Survival in today’s marketplace demands professionals who combine a passion for innovation with the ability to analyze and interpret large volumes of data. The explosive growth of many businesses, government, and scientific databases, over the last decade, has placed an increasing demand for the ability to analyze large sets of data. Much, if not most, of this data is multivariate often with a large number of dimensions. This course is intended as an introduction to the techniques available to analyze multivariate data, along with tools to conduct analyses.
|# Students||Degree||Field of Study||Collaboration Period (days)||International Students (%)|
|25||Master||Business, Computer Science||106||25|
This course focuses on understanding the basic methods underlying multivariate analysis through computer applications using R. Multivariate analysis is concerned with datasets that have more than one response variable for each observational or experimental unit. Topics covered include principal components analysis, factor analysis, structural equation modelling, multidimensional scaling, correspondence analysis, cluster analysis, multivariate analysis of variance, discriminant function analysis, logistic regression, and other methods used for dimension reduction, pattern recognition, classification, and forecasting. Through class exercises and a project, students apply these methods to real data and learn to think critically about data analysis and research findings.
Students will be able to:
– Use a statistical software to analyze multivariate data
– Visualize multivariate data and communicate results
– Recognize pattern, classify information, and forecast events
– Think critically about data and research findings
– Present findings – read and execute multivariate analysis techniques not covered in class
– Help make business recommendations based on results from multivariate analysis
Expected solution proposals
Final project report and presentation
Hypothesis Testing, Data Preparation (Distributions, outliers and missing-value), Multiple Linear Regression, MANOVA, Classifiers (Linear Discriminant Analysis, Logistic Regression, Naive Bayes, K-Nearest Neighbor), Ensembles, Clustering (Hierarchical, K-Means, and Density based), Dimension Reduction (PCA, SVD and Factor Analysis), and Special topics.