Introduction to Analytics Modeling
Master essential analytics models and methods using R programming. Learn to choose and implement the right analytical approaches for business insights.
Master essential analytics models and methods using R programming. Learn to choose and implement the right analytical approaches for business insights.
This comprehensive course, part of the Analytics: Essential Tools and Methods MicroMasters program, provides a deep dive into fundamental analytics models and methods. Students learn to select appropriate data sets, algorithms, and techniques for solving specific business problems. The course covers statistical models, machine learning, classification, clustering, change detection, data smoothing, validation, prediction, optimization, experimentation, and decision making. Through hands-on practice with R programming, students develop practical skills in implementing various analytical models and understanding when to apply specific approaches.
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What you'll learn
Master fundamental analytics models and methods for real-world applications
Develop proficiency in using R programming for implementing analytical models
Learn to select appropriate analytical techniques for specific business problems
Understand statistical modeling and machine learning approaches
Gain expertise in classification, clustering, and change detection methods
Master data smoothing and validation techniques
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 comprehensive coverage of analytical modeling, focusing on fundamental models and methods essential for data analysis and business decision-making. Students learn to use industry-standard tools like R to implement various analytical techniques, including statistical modeling, machine learning, classification, clustering, and optimization. The course emphasizes practical application and understanding of when to use specific models, helping students build a robust analytics toolbox.
Fee Structure
Instructor
4 Courses
A Pioneering Analytics Scholar Bridging Sports, Education, and Innovation
Dr. Joel Sokol, the Harold E. Smalley Professor at Georgia Tech's H. Milton Stewart School of Industrial and Systems Engineering, has established himself as a transformative figure in analytics education and research. As the founding Director of Georgia Tech's interdisciplinary Master of Science in Analytics program, both on-campus and online, he has shaped the future of analytics education. His groundbreaking LRMC method for NCAA basketball tournament predictions has become an industry standard, while his consulting work spans all three major American sports leagues. After earning three bachelor's degrees from Rutgers University in mathematics, computer science, and applied sciences in engineering, he completed his Ph.D. in operations research from MIT. His research portfolio extends beyond sports to include health analytics, supply chain logistics, and military applications, earning him the EURO Management Science Strategic Innovation Prize and a Cozzarelli Prize finalist position. His dedication to education has garnered recognition from the National Academy of Engineering, IISE, INFORMS, and EURO, along with Georgia Tech's highest teaching honors. He served two terms as INFORMS Vice President of Education and pioneered the development of Georgia Tech's online analytics program, which has grown to serve hundreds of students. His work in personalized medicine, particularly in organ transplant optimization, demonstrates his commitment to applying analytics for societal impact.
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