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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.

 

Syllabi

Spring 2024 Download