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The Analytics Edge: Data-Driven Decision Making
Learn powerful data analytics methods to gain a competitive edge in your career and life in this 13-week MIT course.
Learn powerful data analytics methods to gain a competitive edge in your career and life in this 13-week MIT course.
Discover the transformative power of data analytics in MIT's comprehensive course, "The Analytics Edge". This program explores how data is revolutionizing businesses, social interactions, and society at large. Through real-world case studies from Moneyball to Netflix, you'll learn to apply cutting-edge analytics methods including linear and logistic regression, decision trees, text analytics, clustering, visualization, and optimization. Using R, a powerful statistical software, you'll build models and work with diverse datasets. The course combines theoretical knowledge with practical application, enabling you to leverage data for informed decision-making in various fields. Whether you're a beginner or have some experience, this challenging yet rewarding course will equip you with the skills to apply analytics to real-world scenarios, giving you a competitive edge in your career and personal life.
Instructors:
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What you'll learn
Apply linear and logistic regression to real-world data problems
Develop decision tree models for classification and prediction
Perform text analytics and sentiment analysis on unstructured data
Use clustering techniques for pattern recognition in datasets
Create effective data visualizations to communicate insights
Implement optimization models for decision-making
Skills you'll gain
This course includes:
Live video
Weekly homework assignments, Quick questions after lectures, Final exam
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This comprehensive course introduces students to the power of data analytics in transforming businesses and society. Through a series of real-world examples and case studies, students learn to apply various analytics methods to solve complex problems. The curriculum covers a wide range of topics, including linear regression, logistic regression, decision trees (CART), text analytics, clustering, data visualization, and mathematical optimization. Students gain hands-on experience using R, a powerful statistical software, to build models and analyze diverse datasets. The course structure includes lecture videos broken into manageable segments, followed by quick comprehension checks. Weekly recitations provide additional examples and datasets to reinforce learning. Homework assignments involve practical application of methods using R and LibreOffice. The course culminates in a final exam that tests students' ability to apply analytics methods to real-world scenarios. By the end of the course, students will have developed a robust toolkit of analytics skills applicable across various industries and personal decision-making processes.
Fee Structure
Instructors
Pioneering Analytics Scholar Revolutionizing Operations Research
Dimitris Bertsimas serves as the Vice Provost for Open Learning, Associate Dean of Business Analytics, and Boeing Professor of Operations Research at MIT's Sloan School of Management. After earning his PhD from MIT in 1988, he immediately joined the faculty, where he has made transformative contributions to optimization, machine learning, and their practical applications. His research spans multiple industries, including healthcare, finance, and transportation, with over 300 scientific papers and eight books to his credit.
Analytics Education Pioneer and Operations Research Expert
Dr. Allison O'Hair serves as a Lecturer in Management at Stanford Graduate School of Business, following her role as a lecturer at MIT Sloan School of Management. After earning her PhD in Operations Research from MIT in 2013, she has established herself as an expert in analytics and optimization, particularly focusing on healthcare applications. Her significant contributions to analytics education include co-authoring "The Analytics Edge," a comprehensive textbook that provides a unified, modern treatment of analytics through real-world applications
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Frequently asked questions
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