Online courses directory (418)

The Statics of Everyday Objects is a course about how structures react to forces exerted on them while remaining unmoved.

Volume under a surface with double integrals. Triple integrals as well. Double Integral 1. Double Integrals 2. Double Integrals 3. Double Integrals 4. Double Integrals 5. Double Integrals 6. Triple Integrals 1. Triple Integrals 2. Triple Integrals 3. Double Integral 1. Double Integrals 2. Double Integrals 3. Double Integrals 4. Double Integrals 5. Double Integrals 6. Triple Integrals 1. Triple Integrals 2. Triple Integrals 3.

Conditional statements and deductive reasoning. Conditional statements exercise examples. Logical argument and deductive reasoning exercise example. Conditional statements and deductive reasoning. Conditional statements exercise examples. Logical argument and deductive reasoning exercise example.

Let's think more visually about things like shifts, rotations, scaling and symmetry. Axis of symmetry. Translations of polygons. Determining a translation for a shape. Rotation of polygons example. Axis of symmetry. Translations of polygons. Determining a translation for a shape. Rotation of polygons example.

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.

This course is the second installment of Single-Variable Calculus.  In Part I (MA101 [1]), we studied limits, derivatives, and basic integrals as a means to understand the behavior of functions.  While this end goal remains the same, we will now focus on adapting what we have learned to applications.  By the end of this course, you should have a solid understanding of functions and how they behave.  You should also be able to apply the concepts we have learned in both Parts I and II of Single-Variable Calculus to a variety of situations. We will begin by revisiting and building upon what we know about the integral.  We will then explore the mathematical applications of integration before delving into the second major topic of this course: series.  The course will conclude with an introduction to differential equations. [1] http:///courses/ma101/…

Multivariable Calculus is an expansion of Single-Variable Calculus in that it extends single variable calculus to higher dimensions.  You may find that these courses share many of the same basic concepts, and that Multivariable Calculus will simply extend your knowledge of functions to functions of several variables.  The transition from single variable relationships to many variable relationships is not as simple as it may seem; you will find that multi-variable functions, in some cases, will yield counter-intuitive results. The structure of this course very much resembles the structure of Single-Variable Calculus I and II.  We will begin by taking a fresh look at limits and continuity.  With functions of many variables, you can approach a limit from many different directions.  We will then move on to derivatives and the process by which we generalize them to higher dimensions.  Finally, we will look at multiple integrals, or integration over regions of space as opposed to intervals. The goal of Mu…

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.

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.

Math is everywhere. In this class, you’ll gain an in-depth understanding of algebraic principles, many of which you may have seen before, and learn how to use them to solve problems that we encounter in everyday life. The online version of College Algebra will cover all of the topics that you would see in more traditional class formats, but it will present the material in a way that we hope you’ll find fresh and interesting. You will learn about functions, polynomials, graphing, complex numbers, exponential and logarithmic equations, and much more, all through exploring real-world scenarios.

This course has been designed to provide you with a clear, accessible introduction to discrete mathematics. Discrete mathematics describes processes that consist of a sequence of individual steps (as compared to calculus, which describes processes that change in a continuous manner). The principal topics presented in this course are logic and proof, induction and recursion, discrete probability, and finite state machines. As you progress through the units of this course, you will develop the mathematical foundations necessary for more specialized subjects in computer science, including data structures, algorithms, and compiler design. Upon completion of this course, you will have the mathematical know-how required for an in-depth study of the science and technology of the computer age.

2.01x introduces principles of structural analysis and strength of materials in applications to three essential types of load-bearing elements: bars in axial loading, axisymmetric shafts in torsion, and symmetric beams in bending.

The course covers fundamental concepts of continuum mechanics, including internal resultants, displacement fields, stress, and strain.

While emphasizing analytical techniques, the course also provides an introduction to computing environments (MATLAB) and numerical methods (Finite Elements: Akselos)

This course is based on the first subject in solid mechanics for MIT Mechanical Engineering students. Join them and learn how to predict linear elastic behavior, and prevent structural failure, by relying on the notions of equilibrium, geometric compatibility, and constitutive material response.

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.

What ratios and proportions are. Using them to solve problems in the real world. Ratio problem with basic algebra (new HD). Writing proportions. Writing proportions. Find an Unknown in a Proportion. Find an Unknown in a Proportion 2. Proportions 1. Proportions 2 exercise examples. Proportions 2. Constructing proportions to solve application problems. Constructing proportions to solve application problems. The Golden Ratio. Advanced ratio problems. More advanced ratio problem--with Algebra (HD version). Another Take on the Rate Problem. Alternate Solution to Ratio Problem (HD Version). Mountain height word problem. Ratio problem with basic algebra (new HD). Writing proportions. Writing proportions. Find an Unknown in a Proportion. Find an Unknown in a Proportion 2. Proportions 1. Proportions 2 exercise examples. Proportions 2. Constructing proportions to solve application problems. Constructing proportions to solve application problems. The Golden Ratio. Advanced ratio problems. More advanced ratio problem--with Algebra (HD version). Another Take on the Rate Problem. Alternate Solution to Ratio Problem (HD Version). Mountain height word problem.

這是一個機率的入門課程，著重的是教授機率基本概念。另外我們的作業將搭配臺大電機系所開發的多人競技線上遊戲方式，讓同學在遊戲中快樂的學習，快速培養同學們對於機率的洞察力與應用能力。

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.

During this four week course, instructors will learn how to create and teach an exciting new type of developmental math course known as a pathways course. These courses (e.g., Math Literacy for College Students, Quantway