Recent research in the cognitive sciences—especially psychology, economics, and neuroscience—has begun to examine the factors that promote personal well-being and happiness. One surprising, but consistent, observation has been that many of the things that are widely believed to be crucial for our happiness—wealth, material possessions, not missing out, and even good grades—not only fail to make many people happy but can actively undermine the sense of well-being. The course begins by introducing some misconceptions that many people might have about what makes for a satisfying life. We examine the psychological biases and other mental quirks of our minds that make it hard for us to identify what actually makes us happy and then discuss what recent research reveals about factors that promote happiness. Importantly, throughout the course, students are invited to apply these strategies in everyday life. In other words, we both discuss what new results in cognitive science suggest to anyone interested in better understanding the factors that promote happiness and also actively put these scientific findings into practice. Along the way, we review historical and cross-cultural views on the meaning of happiness, discuss the limitations of scientific approaches to the topic, and identify aspects of modern life that make it difficult to develop habits that support happiness. Students may only take one of the following courses for degree or certificate credit: the Harvard Summer School course PSYC S-1022, PSYC E-1022 (offered previously), or PSYC E-1680.

This course builds on CSCI E-101, giving students a solid foundation for advanced data modeling, machine learning, and artificial intelligence (AI). The course focuses on the methods of modern computational statistics underpinning advanced data science. In the twenty-first century, these powerful, computationally intensive algorithms are both practical and widely used. These algorithms enable us to explore and model the large, complex datasets commonly encountered in the real world today. The focus of this course is on methods to address the exploration, inference, and modeling challenges arising from the analysis of increasingly complex datasets. Approaches to large-scale computational statistical inference are discussed, include maximum likelihood, modern resampling methods, and Bayesian models. The properties and behavior of the rich family of linear models and Bayesian models, foundational to many statistical, machine learning, and AI algorithms are surveyed. Additionally, time series models are explored. The course employs a combination of theory and hands-on experience using Python programming tools. The focus is on foundational computational statistical algorithms, statistical inference methods, and effective visualization methods. The hands-on component of the course uses the Python packages, NumPy, Pandas, Seaborn, Statsmodels, and PyMC3, along with selected other open-source packages.

This course examines the science and practice of decision-making through the lens of psychology, behavioral economics, and management. Students explore the cognitive biases, heuristics, and emotional influences that shape decisions and learn how to create value by designing effective decision-making strategies. Anchored in Ellen Langer's principle the psychology of possibility and enriched by key frameworks like prospect theory, the broaden-and-build theory, and growth mindset, the course connects theory with practice. Students tackle real-world scenarios, participate in negotiation simulations, and analyze decision-making through cultural and organizational perspectives. By the end of the course, students have developed the critical thinking skills and practical tools to address complex problems, lead with confidence, and enhance decision-making processes in their organizations and industries.

This course examines the science and practice of decision-making through the lens of psychology, behavioral economics, and management. Students explore the cognitive biases, heuristics, and emotional influences that shape decisions and learn how to create value by designing effective decision-making strategies. Anchored in Ellen Langer's principle the psychology of possibility and enriched by key frameworks like prospect theory, the broaden-and-build theory, and growth mindset, the course connects theory with practice. Students tackle real-world scenarios, participate in negotiation simulations, and analyze decision-making through cultural and organizational perspectives. By the end of the course, students have developed the critical thinking skills and practical tools to address complex problems, lead with confidence, and enhance decision-making processes in their organizations and industries.