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This Ph.D. seminar examines the strategic foundations of innovation and entrepreneurship, which are two critical engines of value creation and competitive advantage. Approximately 75% of the course focuses on innovation (firm capabilities, technological change, ecosystems, industry dynamics), and 25% on entrepreneurship (opportunity recognition, new venture strategies, corporate entrepreneurship).

This course is aimed at developing practical machine learning and data science skills. It will cover various machine learning concepts and methods as well as hands on programming with Python. The students will be able to identify the type of problems and the appropriate machine learning models, strengths and weaknesses of each model, including the assumptions of the method, and apply machine learning for prediction and evaluate the results.

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Presentation of journal reports on business topics utilizing a quantitative approach.

Techniques of multivariate statistical an-alysis as applied to business research. Discriminant analysis, canonical correlation, multivariate analysis of variance and covariance, factor analysis, cluster analysis.

Modeling and solution of decision problems under uncertainty. Topics include stochastic dynamic programming (Markov decision processes), covering both finite and infinite horizon problems; and stochastic linear/integer programming. Several computational techniques and applications are presented.