A Scientific Approach to Innovation Management
Master data-driven decision-making for innovation. Learn to assess, analyze, and implement innovative ideas using scientific methods.
Master data-driven decision-making for innovation. Learn to assess, analyze, and implement innovative ideas using scientific methods.
This course teaches a systematic, data-driven approach to innovation management. It covers how to assess the feasibility of innovative ideas through problem-framing techniques and rigorous data analysis. Students learn to apply scientific methods to innovation decisions, including formulating hypotheses, designing experiments, and interpreting results. The course covers probabilistic thinking, data analysis techniques, and advanced tools like regression analysis and machine learning. Real-world case studies and exercises provide practical application of concepts. Ideal for entrepreneurs, managers, and innovators seeking to make more informed decisions about product or service innovations.
4.6
(72 ratings)
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What you'll learn
Understand how to apply the scientific method to innovation management
Learn to formulate and test hypotheses for innovation decisions
Master basic statistical tools for data analysis in innovation contexts
Design and interpret experiments for testing innovative ideas
Understand advanced concepts like regression analysis and machine learning in innovation
Apply data-driven decision-making techniques to real-world innovation challenges
Skills you'll gain
This course includes:
7 Hours PreRecorded video
6 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This course offers a comprehensive approach to innovation management using scientific methods and data analysis. It begins with an introduction to innovation as problem-solving and the basics of the scientific approach in business contexts. Students learn to formulate problems, develop hypotheses, and design tests for their ideas. The course covers essential statistical concepts, including probability theory and regression analysis, and their application to innovation decisions. It also explores advanced topics such as experimental design, causality analysis, and the basics of machine learning for innovation management. Throughout the course, real-world case studies and examples illustrate how companies apply these methods to make better innovation decisions. The course concludes with a discussion on the limitations and appropriate use of the scientific approach in different business scenarios. A final project allows students to apply their learning to a real-world innovation challenge.
THE INNOVATION DECISION
Module 1 · 2 Hours to complete
THEORY AND DATA FOR INNOVATION MANAGEMENT
Module 2 · 3 Hours to complete
DATA ANALYSIS
Module 3 · 1 Hours to complete
ADVANCED TOOLS FOR INNOVATION MANAGEMENT DECISIONS
Module 4 · 1 Hours to complete
FINAL PROJECT
Module 5 · 4 Hours to complete
Fee Structure
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Instructors
Renowned Expert in Strategic Management, Entrepreneurship, and Innovation
Professor of Corporate Management at Bocconi University in Milan, holds a PhD from Stanford University. His research centers on strategic management, entrepreneurship, and innovation, and he has published extensively in top international journals. His highly cited book, Markets for Technology (co-authored with Ashish Arora and Andrea Fosfuri, MIT Press), has garnered significant attention in the field. He serves as Co-Editor of the Strategic Management Journal, is a Fellow of the Strategic Management Society, and a member of the Center for Economic and Policy Research (CEPR) in London. His academic career includes visiting professorships at Stanford University and MIT's Sloan School, and he has chaired the Business Policy and Strategy Division of the US Academy of Management.
Expert in Business Organization and Lean Transformations
A Professor of Business Organization at Bocconi University, Milan, Italy, this esteemed academic has authored several influential books, including Lean Transformations for Small and Medium Enterprises (CRC-Productivity Press, 2016). His extensive research spans a variety of topics in management and organizational studies, with his work appearing in prestigious journals such as Management Science, Academy of Management Journal, Strategic Management Journal, Organization Science, Research Policy, Industrial and Corporate Change, and the MIT Sloan Management Review.
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4.6 course rating
72 ratings
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