Predictive Analytics for Business Decisions
Learn to build predictive and prescriptive models using numerical data for effective business decision-making in this introductory course.
Learn to build predictive and prescriptive models using numerical data for effective business decision-making in this introductory course.
This course introduces predictive and prescriptive analytics for business decision-making. Participants will learn to differentiate between cross-sectional and longitudinal data, understand prediction versus forecasting, and explore parametric and non-parametric modeling approaches. The course covers key concepts like Linear Programming Problems (LPP) for scenario analysis and the Gradient Descent Algorithm, fundamental to many machine learning techniques. By the end, learners will be equipped to apply data-driven decision-making strategies in various business contexts, from fraud prevention to customer loyalty enhancement.
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What you'll learn
Understand the difference between cross-sectional and longitudinal data
Differentiate between prediction and forecasting problem scenarios
Apply data-led decision making concepts to business situations
Understand parametric and non-parametric modeling approaches
Use Linear Programming Problems for multiple "What if" scenarios in business
Conceptualize the Gradient Descent Algorithm, a foundation for machine learning
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course provides a comprehensive introduction to predictive and prescriptive analytics for business decision-making. It covers the fundamentals of data types, modeling approaches, and key algorithms used in business analytics. Participants will learn to distinguish between different types of data and analytical problems, understand the trade-offs in modeling approaches, and apply concepts like Linear Programming Problems and Gradient Descent Algorithm. The course emphasizes practical application, enabling learners to use data-driven strategies for various business scenarios, from risk management to operational efficiency. By the end of the course, students will have a solid foundation in analytics, preparing them to make more informed, data-backed business decisions.
Fee Structure
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