“Perhaps a man really dies when his brain stops, when he loses the power to take in a new idea.” –George Orwell. Neuroethics might well be the most rapidly growing area within bioethics; indeed, in some respects neuroethics has grown as an independent field, with its own journals, professional society and institutional centers.
Every dog owner should have some understanding of reproduction, whether they intend to breed that dog or just want to have their dog spayed or castrated. As a dog enthusiast, you want to know what is best for your dog and to understand your veterinarian’s recommendations. This course is intended to give you the background knowledge necessary to help you achieve those goals.
In light of recent outbreaks of infectious diseases and new developments in immunizations, everyone from parents to policy-makers have questions about vaccines. What's actually in a vaccine? Are vaccines effective? Are they safe? Should a society require that all citizens get certain vaccines?
This course, based on the award-winning class offered both at the Indian School of Business and at the McCombs School of Business at The University of Texas at Austin, developed by Prof. Raj Raghunathan (aka "Dr. Happy-smarts") draws content from a variety of fields, including psychology, neuroscience, and behavioral decision theory to offer a tested and practical recipe for leading a life of happiness and fulfillment.
Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining.
This course is an applied statistics course focusing on data analysis. The course will begin with an overview of how to organize, perform, and write-up data analyses. Then we will cover some of the most popular and widely used statistical methods like linear regression, principal components analysis, cross-validation, and p-values. Instead of focusing on mathematical details, the lectures will be designed to help you apply these techniques to real data using the R statistical programming language, interpret the results, and diagnose potential problems in your analysis. You will also have the opportunity to critique and assist your fellow classmates with their data analyses.