Courses tagged with "Nutrition" (219)

M2O2C2 provides a first taste of multivariable differential calculus. By introducing the machinery of linear algebra, this course provides helpful tools for understanding the derivative of a function of many variables.

This six week course will help students prepare for the AP* Calculus AB Exam. * AP Calculus is a registered trademark of the College Board, which was not involved in the production of, and does not endorse, this product.

This course is designed for high school students preparing to take the AP* Statistics Exam. * AP Statistics is a registered trademark of the College Board, which was not involved in the production of, and does not endorse, this product.

Learn how to use regression models, the most important statistical analysis tool in the data scientist's toolkit. This is the seventh course in the Johns Hopkins Data Science Specialization.

Statistics 2 at Berkeley is an introductory class taken by about 1,000 students each year. Stat2.3x is the last in a sequence of three courses that make up Stat2x, the online equivalent of Berkeley's Stat 2. The focus of Stat2.3x is on statistical inference: how to make valid conclusions based on data from random samples. At the heart of the main problem addressed by the course will be a population (which you can imagine for now as a set of people) connected with which there is a numerical quantity of interest (which you can imagine for now as the average number of MOOCs the people have taken). If you could talk to each member of the population, you could calculate that number exactly. But what if the population is so large that your resources will not stretch to interviewing every member? What if you can only reach a subset of the population?

Stat 2.3x will discuss good ways to select the subset (yes, at random); how to estimate the numerical quantity of interest, based on what you see in your sample; and ways to test hypotheses about numerical or probabilistic aspects of the problem.

The methods that will be covered are among the most commonly used of all statistical techniques. If you have ever read an article that claimed, "The margin of error in such surveys is about three percentage points," or, "Researchers at the University of California at Berkeley have discovered a highly significant link between ...," then you should expect that by the end of Stat 2.3x you will have a pretty good idea of what that means. Examples will range all the way from a little girl's school science project (seriously – she did a great job and her results were published in a major journal) to rulings by the U.S. Supreme Court.

The fundamental approach of the series was provided in the description of Stat2.1x and appears here again: There will be no mindless memorization of formulas and methods. Throughout the course, the emphasis will be on understanding the reasoning behind the calculations, the assumptions under which they are valid, and the correct interpretation of results.

Learn how to draw conclusions about populations or scientific truths from data. This is the sixth course in the Johns Hopkins Data Science Course Track.

课程从问题开始揭示一些数学思想形成的过程，和听众一起从思想上重走一遍前辈们走过的路，体会数学抽象的魅力。 In this course I share the processes which formed the core concepts of mathematical philosophy, walking with students as they experience, learn and enjoy mathematical abstraction.

By Luca Trevisan

Want to be an engineer or scientist? Lack mathematical confidence? Learn to think mathematically and explore essential concepts.

This course explains how you can use numbers to describe the natural world and make sense of everything from atoms to oceans.

Teaches, as the antidote to rigor mortis, the art of educated guessing and opportunistic problem solving.

Calculus One is a first introduction to differential and integral calculus, emphasizing engaging examples from everyday life.

A wondrously romantic belief is that brilliant thinkers magically produce brilliant ideas: Einstein jostles his hair and relativity falls out. We can enjoy these fanciful visions of leaps of genius, but we should not be fooled into believing that they’re reality.

Brilliant innovators are brilliant because they practice habits of thinking that inevitably carry them step by step to works of genius. No magic and no leaps are involved.

Professor Starbird will discuss how habits of effective thinking and creativity can be taught and learned through puzzles and mathematics. Anyone who practices these habits of mind will inevitably create new insights, new ideas, and new solutions.

本课程以通俗的语言介绍粒子物理的主要内容和最前沿的研究。课程涉及的主要内容包括： 物质和反物质， 夸克和强相互作用，中微子和弱相互作用，对称性和它的作用， 粒子物理的标准模型，粒子天文学， 暗物质。 This course gives an brief introduction of the main content and the frontier of particle physics in popular language. It includes: matter and antimatter, quark and strong interaction, neutrino and weak interaction, symmetry and its applications, standard model of particle physics, particle astronomy, dark matter etc.

This course is taught in French Vous voulez comprendre l'arithmétique ? Vous souhaitez découvrir une application des mathématiques à la vie quotidienne ? Ce cours est fait pour vous ! De niveau première année d'université, vous apprendrez les bases de l'arithmétique (division euclidienne, théorème de Bézout, nombres premiers, congruence). Vous vous êtes déjà demandé comment sont sécurisées les transactions sur Internet ? Vous découvrirez les bases de la cryptographie, en commençant par les codes les plus simples pour aboutir au code RSA. Le code RSA est le code utilisé pour crypter les communications sur internet. Il est basé sur de l'arithmétique assez simple que l'on comprendra en détail. Vous pourrez en plus mettre en pratique vos connaissances par l'apprentissage de notions sur le langage de programmation Python. Vous travaillerez à l'aide de cours écrits et de vidéos, d'exercices corrigés en vidéos, des quiz, des travaux pratiques. Le cours est entièrement gratuit !

This course is designed for students who will be starting or restarting college within the next year, and for current students who have not completed their general education math requirement. It will provide math refresher materials covering a wide range of mathematical concepts together with information about success in college. Incoming college students are typically placed in college math courses based on placement exam scores. Students often take these placement exams with minimal preparation or after a long break since their last math class. The study materials in the course will help students prepare for placement exams, and higher scores mean fewer required math courses in college. Students who have already taken a placement exam (such as the ACT) can also use these materials to study and then retest, hopefully scoring higher. College students who have started, but not finished their math courses, can also retake a placement exam and possibly skip a math class. The course will also be valuable for anyone who just wants to refresh their math skills. The provided study materials are individualized based on a student’s current knowledge. Each student will be provided a customized learning path that maximizes efficiency so that study time is spent where it’s needed most. Beyond math content, the course will also provide college success material such as test-taking strategies, new student orientation, and study techniques. All of this material can be accessed separately from the math content so even if a student is already placed highly in math, or has tested out of it completely, the course will provide valuable information to help the student orient to college and to get the most out of the college experience.

Real Analysis II is the sequel to Saylor’s Real Analysis I, and together these two courses constitute the foundations of real analysis in mathematics. In this course, you will build on key concepts presented in Real Analysis I, particularly the study of the real number system and real-valued functions defined on all or part (usually intervals) of the real number line. The main objective of MA241 [1] was to introduce you to the concept and theory of differential and integral calculus as well as the mathematical analysis techniques that allow us to understand and solve various problems at the heart of sciencenamely, questions in the fields of physics, economics, chemistry, biology, and engineering. In this course, you will build on these techniques with the goal of applying them to the solution of more complex mathematical problems. As long as a problem can be modeled as a functional relation between two quantities, each of which can be expressed as a set of real numbers, the techniques used for real-value…

This class covers the analysis and modeling of stochastic processes. Topics include measure theoretic probability, martingales, filtration, and stopping theorems, elements of large deviations theory, Brownian motion and reflected Brownian motion, stochastic integration and Ito calculus and functional limit theorems. In addition, the class will go over some applications to finance theory, insurance, queueing and inventory models.

Ders çok değişkenli fonksiyonlardaki iki derslik dizinin ikincisidir. Birinci ders türev ve entegral kavramlarını geliştirmekte ve bu konulardaki problemleri temel çözme yöntemlerini sunmaktadır. Bu ders, birinci derste geliştirilen temeller üzerine daha ileri konuları işlemekte ve daha kapsamlı uygulamalar ve çözümlü örnekler sunmaktadır. Ders gerçek yaşamdan gelen uygulamaları da tanıtmaya önem veren “içerikli yaklaşımla” tasarlanmıştır.