Data Science for Business Innovation
Learn data science for business innovation. Gain insights into data-driven decision-making and machine learning for executives and managers.
Learn data science for business innovation. Gain insights into data-driven decision-making and machine learning for executives and managers.
This course offers a comprehensive introduction to Data Science for Business Innovation, designed for executives and managers to foster data-driven innovation. It explains what Data Science is and why it's hyped, covering the value it creates and the main problems it solves. You'll learn about descriptive, predictive, and prescriptive analytics, as well as the roles of machine learning and artificial intelligence. The course covers supervised, unsupervised, and semi-supervised methods, explaining classification, clustering, and regression techniques. It also discusses NoSQL data models and cloud-based computation platforms. All topics are presented with example-based lectures, use cases, and success stories, providing a practical understanding of how to apply data science concepts in business settings.
4.3
(260 ratings)
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What you'll learn
Understand what data science is and its business value
Learn the difference between descriptive, predictive, and prescriptive analytics
Explore the roles of machine learning and artificial intelligence in business
Gain insights into supervised, unsupervised, and semi-supervised learning methods
Understand classification, clustering, and regression techniques
Learn about NoSQL data models and cloud-based computation platforms
Skills you'll gain
This course includes:
63 Minutes PreRecorded video
11 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course provides a comprehensive overview of Data Science for Business Innovation, tailored for executives and managers. It covers the fundamentals of data science, big data, and machine learning, explaining their impact on business value and decision-making. The curriculum includes success stories like Netflix, demonstrating real-world applications of data-driven strategies. Students will learn about various machine learning techniques, including linear regression, classification, decision trees, and clustering, all presented from a business perspective. The course also addresses challenges and risks in implementing data-driven strategies, preparing managers for real-world scenarios.
Introduction to Data-driven Business
Module 1 · 56 Minutes to complete
Terminology and Foundational Concepts
Module 2 · 2 Hours to complete
Data Science Methods for Business
Module 3 · 3 Hours to complete
Challenges and Conclusions
Module 4 · 1 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructors
Leader in Data Science and Software Engineering
Marco Brambilla is a full professor at Politecnico di Milano, where he engages in both research and innovation across industrial and academic sectors. His research interests encompass data science, software modeling languages, design patterns, crowdsourcing, social media monitoring, and big data analysis. He has served as a visiting researcher at CISCO in San José and the University of California, San Diego, as well as a visiting professor at Dauphine University in Paris. Brambilla is the founder of Fluxedo, a startup focused on social media analysis and engagement, and WebRatio, a company that develops software modeling tools for web, mobile, and business process applications. With over 200 publications, including various international books and research articles, he has received multiple best paper awards and delivered keynote speeches at numerous conferences. He leads research projects on data science and collaborates on industrial projects related to data-driven innovation and big data. Additionally, he is the main author of the OMG standard IFML and has participated in several European and international research initiatives. Brambilla has also reviewed FP7 projects and evaluated EU proposals, serving as a program committee chair and member for various conferences while being an associate editor for SIGMOD Records, the Journal of Web Engineering, and Advances in Human-Computing Interactions.
Emanuele Della Valle, Associate Professor and Pioneer in Stream Reasoning at Politecnico di Milano.
Emanuele Della Valle holds a PhD in Computer Science from Vrije Universiteit Amsterdam and a Master's degree in Computer Science and Engineering from Politecnico di Milano, where he currently serves as an associate professor in the Department of Electronics, Information, and Bioengineering. Over nearly 20 years of research, his interests have spanned Data Science, Big Data, Stream Processing, Artificial Intelligence, Semantic technologies, Web Information Retrieval, and Service-Oriented Architectures. He pioneered the field of stream reasoning, which lies at the intersection of Stream Processing and Artificial Intelligence.
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4.3 course rating
260 ratings
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