Courses tagged with "Nutrition" (6413)

Practical Leadership is an interactive seminar where students receive repeated coaching and real-time feedback on their own leadership capabilities from their peers and the instructor. The course is structured around a set of readings. However, the key component is each student's own self-assessment. These self-assessments are done by the students in the first week of the semester. The areas for improvement that the students identify are then targeted in the weekly role plays that are customized for each student in the class. The goal of the class is for each student to increase his or her own leadership abilities through an ongoing cycle of practice, feedback and reflection.

Everyone in education has questions – Practical Learning Analytics is about answering them. To be practical, we’ll focus on data every university records; to keep things interesting, we’ll examine questions raised by many audiences; to ensure impact, we’ll provide realistic data and example code.

Everyone involved in higher education has questions. Students want to know how they’re doing and which classes they should take. Faculty members want to understand their students’ backgrounds and to learn whether their teaching techniques are effective. Staff members want to be sure the advice they provide is appropriate and find out whether college requirements accomplish their goals. Administrators want to explore how all of their students and faculty are doing and to anticipate emerging changes. The public wants to know what happens in college and why.

Everyone has questions. We have the chance to help them find answers.

Practical Learning Analytics has a specific goal: to help us collectively ponder learning analytics in a concrete way. To keep it practical, we will focus on using traditional student record data, the kinds of data every campus already has. To make it interesting, we will address questions raised by an array of different stakeholders, including campus leaders, faculty, staff, and especially students. To provide analytic teeth, each analysis we discuss will be supported by both realistic data and sample code.

Learn the basic components of building and applying prediction functions with an emphasis on practical applications. This is the eighth course in the Johns Hopkins Data Science Specialization.

This course provides a thorough introduction to the C programming language, the workhorse of the UNIX operating system and lingua franca of embedded processors and micro-controllers. The first two weeks will cover basic syntax and grammar, and expose students to practical programming techniques. The remaining lectures will focus on more advanced concepts, such as dynamic memory allocation, concurrency and synchronization, UNIX signals and process control, library development and usage. Daily programming assignments and weekly laboratory exercises are required. Knowledge of C is highly marketable for summer internships, UROPs, and full-time positions in software and embedded systems development.

Internet Enduring Material Sponsored by Stanford University School of Medicine

By Leslie Hill and Helen Paris

This is a course in how corporations make use of the insights and tools of risk management. Most courses on derivatives, futures and options, and financial engineering are taught from the viewpoint of investment bankers and traders in the securities. This course is taught from the point of view of the manufacturing corporation, the utility, the software firm—any potential end-user of derivatives, but not the dealer. Most related courses focus on the extensive taxonomy of instruments and the complex models developed to price them, and on ways to exploit mispricing. While this course will make use of some of these pricing models, the focus is on how corporations use the insights and models to improve their operations, to increase the value of their real assets, or to create the financial flexibility necessary to implement their core strategy.

This course introduces students to the techniques of participatory action research (PAR) and the practice of case study research. PAR processes are place or case-specific, place a premium on local ways of knowing, and gauge the success of research in terms of what partner-communities do with the knowledge that is co-produced. The objective of PAR is to generate the ideas, information, and understandings that ought to inform efforts to promote social change. By focusing on ways of co-producing knowledge using various forms of data collection and analysis, students will learn how the people and communities who are often university partners in applied social science research can use findings or results from PAR case studies to address the challenges they confront in their communities.

Learn more about Participatory Action Research at MIT.

What social and ideological mechanisms allowed Jews to survive and even flourish in Catholic Italy? And under what circumstances did the practice of tolerance break down? This course takes a different approach to the idea of tolerance, as well as to the long, complicated history of the Catholic Church and the Jews.

For the first hundreds of years of modern medicine, progress was a process of trial and error, and to be honest, it was often fatal error, such as bloodletting, which killed the first US president George Washington. Where diseases were clearly understood, and where treatments obviously worked on most patients, such as penicillin for pneumonia, there was no need to delve deeper into questions of effectiveness. But for most conditions, the treatment benefits were modest and there was a need for improved ways of telling whether a new intervention was better than the previous standard treatment. And so scientists introduced a testing procedure for new treatments - the randomized controlled trial.

As the cost of developing new treatments grew, it became very important to detect the slightest improvement that could be attributed to the new drug in order to get it licensed and marketed. So the usual randomized trial was answering the following question: does this treatment work (for otherwise well people with just this single disease, with perfect adherence, treated by the best doctors, under idealised conditions)? But most patients have multiple diseases, imperfect adherence, ordinary doctors and non-ideal circumstances. These patients want to know whether a new treatment is likely to reduce important harms for ordinary people like them, receiving usual care. This mismatch meant that most trials were overestimating the real world effectiveness of new interventions.

In this MOOC, we are going to study this mismatch, and see what its effect is on modern medical care. One example of the studies we will discuss is the VIGOR trial. This influential drug trial resulted in the widespread global overuse of this drug, at a cost of wasted billions of dollars. Ironically, this drug, intended to reduce a problem in individuals, ended up causing more of the same problem in the population!

After we understand the consequences of designing the wrong kinds of trials, we will also look at how best to design pragmatic randomized trials, which answer the important questions that ordinary patients and decision makers in the real world most often ask. We will also explore how these can be combined with economic evaluations and qualitative research – to answer important questions about how much these interventions cost, and how they are experienced by the recipients.

This course is the third and final part of our graduate introduction to semantics. The other two classes are 24.970 Introduction to Semantics and 24.973 Advanced Semantics. The semester will be divided into somewhat independent units. One unit will be devoted to conversational implicatures (mainly scalar implicatures) and another to presupposition. In each unit, we will discuss basic concepts and technical tools and then devote some time to recent work which illustrates their application.

This course provides students with a comprehensive introduction to computers. Students will explore a variety of topics in computing, such as the following: the components of a computer, common computer terminology, an introduction to the Internet, computer security and privacy, computer troubleshooting techniques, and steps to maintain the life of your computer. Through readings and videos, students will learn how to fully understand the basics of computer technology.