Probability and Statistics
A study of the fundamental techniques used in descriptive statistics as applied to real-world data and the processes associated with the design and analysis of experiments; application of theories from calculus to the construction of cumulative distributions for continuous random variables and computation of associated probabilities, expected values and variances.
Gaining a competitive advantage in today’s business environment increasingly demands that organizations know how to innovate. Creativity, continuous improvement, and the ability to turn ideas into action are critical to standing out above the rest. Specific topics will include: the innovation process, disruptive technologies, why plans are bad, and when NOT to listen to your customers. We will also apply our knowledge via an innovation simulation.
This course introduces students to topics in the Machine Learning area of Artificial Intelligence. It will include an introduction to some popular algorithms computers use to make decisions and predictions based on problems consisting of varied types of data. In addition to utilizing the algorithms themselves, students will learn about different methods of evaluating these algorithms and how to choose an algorithm for a particular problem.
An introduction to basic statistical measurements: sampling theory, including estimation of parameters, hypothesis testing and basic decision theory. Other topics include correlation analysis, time series analysis, seasonal fluctuations, trend fitting, and cyclical measurement.
This course focuses on evaluating and analyzing different types of business-related data and developing effective solutions. It will utilize current spreadsheet and database software as tools to facilitate the interpretation of the data. The course will have a lab component requiring student laptop computers equipped with spreadsheet and database software.
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