A quick tour on Big Data and Business Intelligence
Master key concepts of Big Data and the fundamentals of Business Intelligence to enhance your skills in modern data analysis and decision-making.
Master key concepts of Big Data and the fundamentals of Business Intelligence to enhance your skills in modern data analysis and decision-making.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Autonomous Vehicle Engineering Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.2
(18 ratings)
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What you'll learn
Manage big data effectively
Build a comprehensive big data framework
Extract value from big data
Design AI processes
Implement data analysis workflows
Skills you'll gain
This course includes:
0.6 Hours PreRecorded video
4 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This introductory course provides beginners and practitioners with a comprehensive overview of Big Data and Business Intelligence fundamentals. From CAP theorem to Turing assumption, students explore essential concepts in data management, analysis, and AI applications.
Big data basics
Module 1 · 2 Hours to complete
Hadoop
Module 2 · 1 Hours to complete
Hadoop Tools
Module 3 · 1 Hours to complete
NoSQL Systems and AI
Module 4 · 1 Hours to complete
Fee Structure
Instructor
Data Science Expert and Big Data Analytics Specialist
Dr. Elio Masciari serves as Associate Professor at the University of Naples Federico II, specializing in information processing systems and big data analytics. Through his Coursera course "Software for Big Data," he helps students understand complex data management and analysis techniques. His research focuses on big data analytics, fake news detection, sentiment analysis, and veterinary clinical records analysis. Previously a senior researcher at ICAR-CNR, he has authored over 160 publications in international journals and conferences. His expertise spans data management, machine learning, and behavioral modeling, with recent work focusing on COVID-19 sentiment analysis and suspicious activity detection in user behavior. As an advisor for numerous master's and Ph.D. theses, he continues to shape the next generation of data scientists while maintaining active research in big data applications and information processing systems. His contributions have earned him the National Scientific Qualification for Full Professor.
Testimonials
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4.2 course rating
18 ratings
Frequently asked questions
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