What will you in Operations Analytics Course
Understand how to model future demand uncertainties and predict outcomes of competing policy choices.
Develop optimization models to identify the best decisions in settings with low uncertainty.
Apply simulation models to evaluate complex business decisions in uncertain settings.
Utilize decision trees to evaluate decisions made under uncertainty.
Gain insights into real-world business challenges and learn methods to tackle these challenges quantitatively.
Program Overview
1. Introduction, Descriptive and Predictive Analytics
⏱ Duration: 1 week
Introduction to the Newsvendor problem, a fundamental operations problem of matching supply with demand in uncertain settings.
Foundations of descriptive analytics for operations.
Use of historical demand data to build forecasts for future demand.
Introduction to underlying analytic concepts, such as random variables, descriptive statistics, common forecasting tools, and measures for judging the quality of forecasts.
2. Prescriptive Analytics, Low Uncertainty
⏱ Duration: 1 week
Building optimization models and applying them to specific business challenges.
Use of algebraic formulations to express optimization problems.
Conversion of algebraic models into spreadsheet formats.
Utilization of spreadsheet Solvers as tools for identifying the best course of action.
3. Predictive Analytics, Risk
⏱ Duration: 1 week
Building and interpreting simulation models to evaluate complex business decisions in uncertain settings.
Introduction to common measures of risk and reward.
Use of simulation to estimate quantities and interpretation of simulation results.
4. Prescriptive Analytics, High Uncertainty
⏱ Duration: 1 week
Introduction to decision trees for evaluating decisions made under uncertainty.
Integration of optimization, simulation, and decision trees to solve complex business problems with high degrees of uncertainty.
Application of the Newsvendor problem using simulation and optimization frameworks.
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Job Outlook
High demand for operations analytics skills across various industries.
Professionals with these skills are more likely to be promoted and take on leadership roles.
Beneficial for entrepreneurs managing diverse responsibilities.
Freelancers and remote workers can improve workflow and output consistency
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Related Reading
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Specification: Operations Analytics Course
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FAQs
- Basic understanding of math is helpful but not mandatory.
- Course introduces key concepts step by step.
- Practical examples simplify complex analytics.
- Additional resources are available for those needing reinforcement.
- Suitable for beginners and professionals alike.
- Focuses on practical, actionable analytics methods.
- Includes case studies and examples of business challenges.
- Teaches modeling, simulation, and decision-making tools.
- Helps translate analytics concepts into business decisions.
- Less theory-heavy than standard textbooks.
- Covers modeling future demand uncertainties.
- Introduces simulation and decision trees for complex decisions.
- Explains risk and reward measures in analytics.
- Combines optimization, simulation, and predictive methods.
- Prepares learners for real-world operational challenges.
- Business analysts and operations managers.
- Entrepreneurs managing supply chains and operations.
- Professionals in logistics, production, and consulting.
- Students preparing for data-driven careers.
- Freelancers and remote workers improving workflow efficiency.
- High demand for operations analytics skills across industries.
- Enhances ability to solve real-world business problems.
- Improves chances for promotion and leadership opportunities.
- Builds credibility with a certificate of completion.
- Useful for both career advancement and entrepreneurial endeavors.
