With the rapid advances in information technologies, the volume of data generated has tremendously increased in recent years. These data are generated from various sources such as online transactions, mobile applications, sensors, video-capturing systems, and social media, and are arriving at a scale of terabytes or even petabytes. There are ample opportunities for businesses to gain managerial and strategic insights by gathering, cleaning, and analyzing these data.<br><br>Program Educational Goals 1. Apply data analysis methods to explain and enable better decision-making and storytelling. 2. Model and create databases based on business requirements and query databases using query languages. 3. Understand end-to-end machine learning workflow and basic supervised and unsupervised learning algorithms. 4. Engage in data collection, cleaning, and management. 5. Talk knowledgeably and confidently about how to apply machine learning tools to solve real world business problems. 6. Obtain the skills necessary to understand and learn new tools. 7. Explore the behavioral and organizational foundations of ethical and unethical workplace behavior. Apply theories of ethics to guide future decision-making. 8. Choose appropriate probability distributions for modelling real-world phenomena. 9. Use computational Bayesian inference tools to allocate plausibility over potential generative models of real-world phenomena. 10. Communicate data-driven insights and recommendations using plain language and compelling data visualizations. 11. Apply machine learning techniques to analyze data. 12. Recognize problems in business situations where formal management science techniques can be applied. 13. Develop skills for data gathering, model formulation, and analysis of results to aid in decision-making. 14. Distinguish between types of models based on data available/analysis needs: from optimization, decision analysis, simulation, and others, recognize each method's limitations and applicability. 15. Analyze problems and communicate solutions for implementation using case data and software. 16. Explore the role of analytics in guiding business decision-making. 17. Master machine learning techniques to deal with unstructured data. 18. Design and implement causal analytics techniques to support core business decisions. 19. Solve real-world business problems with analytical models and tools.