Predictive, Prescriptive Analytics for Decision Making

Understand the difference between Cross sectional and Longitudinal dataDifferentiate between a prediction and forecasting problem scenario and apply these concepts towards data led decision making.Understand Parametric and Non Parametric modelling approach towards addressing the key tradeoff between Predictive accuracy and Explain- ability of models.Use LPP towards building multiple “What if “ scenarios which are widely used in business decision making.Conceptualize Gradient Descent Algorithm which is a key foundation for most of the widely used Machine learning algorithms to be introduced subsequently.PREDICTIVE, PRESCRIPTIVE ANALYTICS FOR BUSINESS DECISION MAKINGLEARN HOW TO BUILD PREDICTIVE AND PRESCRIPTIVE MODELS USING NUMERICAL DATAPrescriptive analytics can cut through the clutter of immediate uncertainty and changing conditions. It can help prevent fraud, limit risk, increase efficiency, meet business goals, and create more loyal customers.Prescriptive analytics is a type of data analytics—the use of technology to help businesses make better decisions through the analysis of raw data. Specifically, prescriptive analytics factors information about possible situations or scenarios, available resources, past performance, and current performance, and suggests a course of action or strategy. It can be used to make decisions on any time horizon, from immediate to long term.What will you Learn?Understand the difference between Cross sectional and Longitudinal data.Differentiate between a prediction and forecasting problem scenario and apply these concepts towards data led decision making.Understand Parametric and Non Parametric modelling approach towards addressing the key tradeoff between Predictive accuracy and Explain- ability of models.Use LPP towards building multiple “What if “ scenarios which are widely used in business decision making.Conceptualize Gradient Descent Algorithm which is a key foundation for most of the widely used Machine learning algorithms to be introduced subsequently.Top skills you will learnDevelop predictive and prescriptive models using numerical dataTime-series ForecastingOptimization through Linear ProgrammingGradient Descent and it’s applicability in Machine LearningFramework towards business decisionsIdeal For1 – 8 yrs work experience.- Engineering, Math/Statistics/Programming background preferredTypical roles: Domain experts, Engineers, Software and IT Professionals, ProjectManagers, Business Analysts, Consultants, Entrepreneurs.Engineers with over 5 years of experienceWho this course is for:1 – 8 yrs work experienceEngineeringEngineers with over 5 years of experienceDomain expertsEngineersConsultantsEntrepreneursSoftware and IT ProfessionalsProject ManagersBusiness AnalystsMath/Statistics/Programming background preferred Typical roles