MGT6785
Applied Predictive Analytics
This course will explore the foundations of predictive analytical techniques on various data sets to guide the decision-making process. A variety of crucial topics will be addressed. Topics may include the following: (1) descriptive modeling techniques such as principal component analysis and clustering algorithms (i.e., k-means algorithm) and (2) predictive modeling techniques such as decision trees, linear regression, logistic regression and the k-Nearest Neighbor (k-NN). Other topics may be covered if time permits.
PREREQUISITE:
MGT6460 – Applied Business Analytics
COURSE COMPETENCIES:
UPON COMPLETION OF THE COURSE, THE STUDENT WILL BE COMPETENT IN:
- Defining the term ‘predictive analytics.’
- Differentiating between supervised and unsupervised learning.
- Discussing the key differences between parametric and nonparametric models.
- Understanding some of the common obstacles and challenges in using predictive analytics.
- Describing the importance of both understanding and cleansing the data prior to doing any analytic technique aimed at gaining actionable insight from the data.
- Conducting a principal component analysis on a data set using software and interpreting the results.
- Comparing and contrasting clustering versus classification algorithms.
- Conducting a k-means algorithm on a data set using software and interpreting the results.
- Comparing and contrasting both linear and logistic regression. Understand the similarities and differences.
- Explaining the results of a decision tree analysis and its implications for decision-making.
- Conducting a regression analysis (both linear and logistic) on a data set using software and interpreting the results.
- Conducting a k-NN analysis on a data set using software and interpreting the results.