The Analytics for Decision Making specialization by University of Minnesota delves into the core concepts of prescriptive analytics, providing a comprehensive understanding of predictive modeling, linear optimization, and simulation techniques.
Throughout this program, you will gain insights into the four pillars of analytics - Descriptive, Predictive, Causal, and Prescriptive Analytics. You will learn to model and solve decision-making problems using predictive models, linear optimization, and simulation methods.
This specialization is ideal for individuals seeking to enhance their skills in business analytics and decision-making processes. No prior programming knowledge is required, making it accessible to a wide audience.
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The Analytics for Decision Making specialization comprises modules on predictive modeling, linear optimization for decision-making, advanced models for decision-making, and simulation models for solving business problems.
The Introduction to Predictive Modeling course provides a solid foundation in predictive modeling, focusing on linear regression and time series forecasting models. By the end of the course, you will be adept at fitting models to data, interpreting results, and using Excel for predictive modeling techniques.
Optimization for Decision Making introduces the principles of linear optimization, demonstrating how to convert problem scenarios into mathematical models for solving using Excel solver and spreadsheet.
Advanced Models for Decision Making explores real-world decision-making scenarios in various industries, teaching how to connect data and models to formulate solutions using linear optimization and Excel spreadsheet.
Simulation Models for Decision Making equips students with advanced Excel techniques to model and execute simulation models, allowing exploration of various business outcomes and protection against uncertainties.
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