COMPETING THROUGH ANALYTICS

OTM 714

The ability to extract information from data has become essential for companies that want to remain competitive - and therefore essential for students aspiring to become successful managers. Provides an understanding of various analytics methodologies and concepts, and the ability to apply these to business problems related to supply chain management and operations management. Key topics include Data Visualization, Time Series Forecasting, Linear Regression, Classification Methods, Association Rules, Cluster Analysis and Text Mining.

OTM 453

Focuses on the application of analytical methodologies to problems that arise in the context of a company's operations and supply chains. Touches on all three dimensions of analytics (descriptive, predictive, and prescriptive). Emphasis on data and real industry data collected from the university, alumni, and executive board members when possible. Explore, analyze, and utilize such data in a hands-on way, using a variety of software tools. Significantly driven by a set of case problems as opposed to systematic coverage of methodologies.

COMARTS/LIS 705

Introduces key stages in the processes of gathering and analyzing data for decision making, including tasks, methods, and tools used at each stage. Topics include developing the research question from organizational goals, choosing appropriate data collection methods, sampling, basics of measurement and question design, managing and visualizing data, descriptive statistics and basic inferential statistics such as correlations, regressions, and ANOVA.

GENBUS 657

An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.

MHR 614

Technological advances have led many organizations to adopt a more "data-driven" approach to managing their employees. It is therefore critical for managers to understand how to integrate data analytics into their organization's human resource management or risk being at a competitive disadvantage. Build hands-on skills to analyze data in ways that facilitate the development and evaluation of human capital policies, practices, programs, and strategies. Learn to take advantage of data and analytics in decision-making processes. Emphasis on experiential learning using data from various organizational contexts.