• Develops models and tools of data analytics that are used to transform businesses and industries, using examples and case studies in e-commerce, healthcare, social media, high technology, criminal justice, the internet, and beyond. Covers analytics methods such as linear regression, logistic regression, classification trees, random forests, neural networks, text analytics, social network analysis, time series modeling, clustering, and optimization. Uses mostly R programming language and some work in Jupyter notebooks. Includes team project. Meets with 15.071 when offered concurrently. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details.

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