Courses tagged with "Structural engineering" (124)

In this college-level Precalculus course, you will prepare for calculus by focusing on quantitative reasoning and functions. You’ll develop the skills to describe the behavior and properties of linear, exponential, logarithmic, polynomial, rational, and trigonometric functions.

Content in this course will be adaptive, allowing you to achieve mastery in a certain concept before moving on to the next. Utilizing the ALEKS learning system, students in this personalized, self-paced course will be instructed on the topics they are most ready to learn while also providing individualized coaching as you move through each topic.

Before taking this course, you should already have a strong understanding of algebraic skills such as factoring, basic equation solving, and the rules of exponents and radicals.

This 3 credit hour course satisfies the Mathematical Studies (MA) general studies requirement at Arizona State University. This course may satisfy a general education requirement at other institutions; however, it is strongly encouraged that you consult with your institution of choice to determine how these credits will be applied to their degree requirements prior to transferring the credit.

Students often encounter grave difficulty in calculus if their algebraic knowledge is insufficient. This course is designed to provide students with algebraic knowledge needed for success in a typical calculus course. We explore a suite of functions used in calculus, including polynomials (with special emphasis on linear and quadratic functions), rational functions, exponential functions, and logarithmic functions. Along the way, basic strategies for solving equations and inequalities are reinforced, as are strategies for interpreting and manipulating a variety of algebraic expressions. Students enrolling in the course are expected to have good number sense and to have taken an intermediate algebra course.

Preparing for the AP Calculus AB exam requires a deep understanding of many different topics in calculus as well as an understanding of the AP exam and the types of questions it asks. This course is Part 1 of our XSeries: AP Calculus AB and it is designed to prepare you for the AP exam.

In Part 1, you will be learning about limits and derivatives. Limits are an integral part of calculus and many important ideas, definitions, formulas and theorems in calculus are derived from a limit. Derivatives are used to describe the rate of change of one variable with respect to another variable, allowing you to understand change in a variety of contexts. You will be learning to apply derivatives to real-world applications such as related rates, optimization, and growth/decay models.

As you work through this course, you will find lecture videos taught by expert AP calculus teachers, practice multiple choice questions and free response questions that are similar to what you will encounter on the AP exam and tutorial videos that show you step-by-step how to solve problems. By the end of the course, you should be ready to take on the AP exam!

Preparing for the AP Calculus AB exam requires a deep understanding of many different topics in calculus as well as an understanding of the AP exam and the types of questions it asks. This course is Part 1 of our XSeries: AP Calculus AB and it is designed to prepare you for the AP exam.

In Part 2, you will use and apply the meaning and interpretations of derivatives from Part 1 to the integral, antiderivatives and differential equations. You will learn some applications of integrals including finding volumes of solids and solids of revolution, volumes with known cross sections and applications to Velocity-Time graphs. We will close with an introduction to differential equations and see how they are used.

As you work through this course, you will find lecture videos taught by expert AP calculus teachers, practice multiple choice questions and free response questions that are similar to what you will encounter on the AP exam and tutorial videos that show you step-by-step how to solve problems.  By the end of the course, you should be ready to take on the AP exam!

This course will introduce students to the major concepts and tools for collecting, analyzing and drawing conclusions from data. Students are exposed to four broad conceptual themes:

1. Exploring Data: Describing patterns and departures from patterns
2. Sampling and Experimentation: Planning and conducting a study
3. Anticipating Patterns: Exploring random phenomena using probability and simulation
4. Statistical Inference: Estimating population parameters and testing hypotheses

Learn more about our High School and AP* Exam Preparation Courses

* Advanced Placement and AP are registered trademarks of the College Board, which was not involved in the production of, and does not endorse, these offerings.

Our capacity to collect and store data has exponentially increased, but deriving information from data from a scientific perspective requires a foundational knowledge of probability.

Are you interested in a career in the emerging data science field, or as an actuarial scientist? Or want better to understand statistical theory and mathematical modeling?

In this statistics and data analysis course, we will provide an introduction to mathematical probability to help meet your career goals in the exciting new areas becoming known as information science.

In this course, we will first introduce basic probability concepts and rules, including Bayes theorem, probability mass functions and CDFs, joint distributions and expected values.

Then we will discuss a few important probability distribution models with discrete random variables, including Bernoulli and Binomial distributions, Geometric distribution, Negative Binomial distribution, Poisson distribution, Hypergeometric distribution and discrete uniform distribution.

To continue learning about probability, enroll in Probability: Distribution Models & Continuous Random Variables, which covers continuous distribution models, central limit theorem and more.

 
The Center for Science of Information, a National Science Foundation Center, supports learners by offering free educational resources in information science.

In this statistics and data analysis course, you will learn about continuous random variables and some of the most frequently used probability distribution models including, exponential distribution, Gamma distribution, Beta distribution, and most importantly, normal distribution.

You will learn how these distributions can be connected with the Normal distribution by Central limit theorem (CLT). We will discuss Markov and Chebyshev inequalities, order statistics, moment generating functions and transformation of random variables.

This course along with the recommended pre-requisite, Probability: Basic Concepts & Discrete Random Variables, will you give the skills and knowledge to progress towards an exciting career in information and data science.

 
The Center for Science of Information, a National Science Foundation Center, supports learners by offering free educational resources in information science.

Quantum computation is a remarkable subject building on the great computational discovery that computers based on quantum mechanics are exponentially powerful. This course aims to make this cutting-edge material broadly accessible to undergraduate students, including computer science majors who do not have any prior exposure to quantum mechanics. The course starts with a simple introduction to the fundamental principles of quantum mechanics using the concepts of qubits (or quantum bits) and quantum gates. This treatment emphasizes the paradoxical nature of the subject, including entanglement, non-local correlations, the no-cloning theorem and quantum teleportation. The course covers the fundamentals of quantum algorithms, including the quantum fourier transform, period finding, Shor's quantum algorithm for factoring integers, as well as the prospects for quantum algorithms for NP-complete problems. It also discusses the basic ideas behind the experimental realization of quantum computers, including the prospects for adiabatic quantum optimization and the D-Wave controversy.

Before your course starts, try the new edX Demo where you can explore the fun, interactive learning environment and virtual labs. Learn more.

Do I need a textbook for this class?
No. Notes will be posted each week. If you wish to consult other references, a list of related textbooks and online resources will be provided.

What is the estimated effort for course?
About 5-12 hrs/week.

Why is the work load range so wide?
How long you spend on the course depends upon your background and on the depth to which you wish to understand the material. The topics in this course are quite open ended, and will be presented so you can understand them at a high level or can try to follow it at a sophisticated level with the help of the posted notes.

How much does it cost to take the course?
Nothing! The course is free.

Will the text of the lectures be available?
Yes. All of our lectures will have transcripts synced to the videos.

Do I need to watch the lectures live?
No. You can watch the lectures at your leisure.

Quantum Mechanics for Everyone is a four-week long MOOC that teaches the basic ideas of quantum mechanics with a method that requires no complicated math beyond taking square roots (and you can use a calculator for that). Quantum theory is taught without “dumbing down” any of the material, giving you the same version experts use in current research. We will cover the quantum mystery of the two-slit experiment and advanced topics that include how to see something without shining light on it (quantum seeing in the dark) and bunching effects of photons (Hong-Ou-Mandel effect).

To get a flavor for the course and see if it is right for you, watch "Let's get small", which shows you how poorly you were taught what an atom looks like, and "The fallacy of physics phobia." 

Please note: the four sections of this course will be released on a weekly basis from April 18, 2017 to May 9, 2017, when all the course material will be available and the course will become fully self-paced.

Have you ever wondered how you can apply math and science skills to real life? Do you wish you could go beyond what you've learned in the classroom? This science course will advance your knowledge as we unpack some important scientific thinking skills using real-world examples. By completing this course, you will be better prepared to continue studying math and science at the high school level and beyond.

In this course, a collaboration between The University of Queensland and Brisbane Grammar School, we will cover key scientific concepts related to:

• Measurement
• Estimation
• The validity of evidence
• The difference between logic and opinion
• Misconceptions
• Modeling
• Prediction
• Extrapolation

Each concept will be explored through real world examples and problems that will help you visualize how math and science work in your life.

This course is ideal for high school students looking to challenge themselves and further develop an interest in math and science. It is also applicable to high school science teachers looking for additional materials for teaching.

The study of the night sky instilled wonder in our ancestors. Modern astronomy extends the human view to previously unexplored regions of space and time. In this course, you will gain an understanding of these discoveries through a focus on relativity—Einstein's fascinating and non-intuitive description of the physical world. By studying relativity and astronomy together, you will develop physical insight and quantitative skills, and you’ll regain a profound sense of wonder for the universe we call home.

FAQ

• What topics will the course cover?
  • Section One—Introduction
  • Section Two—3, 2, 1 … Launching the journey into spacetime
  • Section Three—Special relativity: from light to dark
  • Section Four—General relativity: from flat to curved

• Is there a required textbook?

  • No textbook is required. Notes will be posted weekly. A list of supplemental resources, including textbooks, will be provided.​​

• What are the learning outcomes of this course?

  • Explain the meaning and significance of the postulates of special and general relativity.

  • Discuss significant experimental tests of both special and general relativity.

  • Analyze paradoxes in special relativity.

  • Apply appropriate tools for problem solving in special relativity.

  • Describe astrophysical situations where the consequences of relativity qualitatively impact predictions and/or observations.

  • Describe daily situations where relativity makes a difference.

Whether you'd like a refresher on the differences between descriptive and inferential statistics or are looking to hone your ability to analyze residuals, this course's video lessons have got you covered. Instructors discuss topics ranging from sample variance and box plots to conditional probabilities and z-scores. Areas of study addressed in this course include:

The job of a data scientist is to glean knowledge from complex and noisy datasets.

Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

In this course, part of the Data Science MicroMasters program, you will learn the foundations of probability and statistics. You will learn both the mathematical theory, and get a hands-on experience of applying this theory to actual data using Jupyter notebooks.

Concepts covered included: random variables, dependence, correlation, regression, PCA, entropy and MDL. 

Too much mathematical rigor teaches rigor mortis: the fear of making an unjustified leap even when it lands on a correct result. Instead of paralysis, have courage: Shoot first and ask questions later. Although unwise as public policy, it is a valuable problem-solving philosophy and the theme of this course: how to guess answers without a proof or an exact calculation, in order to develop insight.

You will learn this skill by mastering six reasoning tools---dimensional analysis, easy cases, lumping, pictorial reasoning, taking out the big part, and analogy. The applications will include mental calculation, estimating population growth rates, understanding drag without differential equations, singing musical intervals to estimate logarithms, approximating integrals, summing infinite series, and turning differential equations into algebra.

Your learning will be supported by regular readings that you discuss with other students, by short tablet videos, by quick problems to help you check your understanding, by weekly homework problems, review and and a final exam. You will work hard, and, by the end of the course, have learned a rough-and-ready approach to using mathematics to understand the world.

All required readings are available within the courseware, courtesy of The MIT Press. A print version of the course textbook, Street-Fighting Math, is also available for purchase. The MIT Press is offering enrolled students a special 30% discount on books ordered directly through the publisher’s website. To take advantage of this offer, please use promotion code SFM30 at The MIT Press. 

FAQ

• Do I need to buy a textbook?
  • Back in 2010, MIT Press agreed to publish the textbook, *Street-Fighting Mathematics*, under a free license (in print and online).
  • Thus, the book is legally available all over the internet, including on this course platform.
  • As a registered student in this course, you can also purchase a printed book from MIT Press at a discount.
• Do you often get into street fights?
  • The last time was in high school, when I was attacked for being “different” and suspended for fighting back.
  • However, in my problem-solving fights (and now that I’m older!), I regularly use reasoning tools and we’ll do the same in this course.

This education and teacher training course will help you blend secondary math instruction with real-world discussion topics such as inequity, poverty, and privilege. Throughout this course, we will also have the opportunity to explore multiple math activities and resources including how to leverage real-world data for classroom instruction.

This course is about the Laplace Transform, a single very powerful tool for understanding the behavior of a wide range of mechanical and electrical systems: from helicopters to skyscrapers, from light bulbs to cell phones. This tool captures the behavior of the system and displays it in highly graphical form that is used every day by engineers to design complex systems.

This course is centered on the concept of the transfer function of a system. Also called the system function, the transfer function completely describes the response of a system to any input signal in a highly conceptual manner. This visualization occurs not in the time domain, where we normally observe behavior of systems, but rather in the “frequency domain.” We need a device for moving from the time domain to the frequency domain; this is the Laplace transform.

We will illustrate these principles using concrete mechanical and electrical systems such as tuned mass dampers and RLC circuits.

Learn the theory of linear algebra hand-in-hand with the practice of software library development.

PHYS201x follows introductory physics courses with a more detailed treatment of oscillators, waves on strings, and electromagnetic waves. In addition to deriving and solving the wave equation, mathematical methods will be introduced on making approximations, describing oscillations with complex numbers, and synthesizing functions with Fourier series. Optical reflection and refraction will be derived, as well as the lens equation and elements of geometrical optics. Optical interference, diffraction, and polarization will be covered in detail, including the role of diffraction in image formation. PHYS201x will have weekly video lectures that explain the material through detailed derivations and demonstrations. There will be weekly homework, a discussion forum, and two exams. Eight weeks of content will be presented, and one week devoted to each self-paced exam.

课程介绍视频也可以访问中国网站

点击上方绿色按钮报名。

计算机是现代社会中用于解决问题的重要工具。利用计算机解决实际问题需要将问题抽象，并对数据进行操作，最后通过计算机程序求解问题。而本门课程主要内容就是对以上内容进行研究。

图灵奖获得者N.Wirth写了一本经典著作“程序=算法+数据结构”。数据结构，是抽象的表示数据的方式；算法，则是计算的一系列有效、通用的步骤。算法与数据结构是程序设计中相辅相成的两个方面。

我 们会围绕着“算法+数据结构=程序”的思路，以问题求解为导向进行学习。希望能够帮助大家提高理论、抽象、设计的能力。在扎实的经典理论基础上，运用问题 抽象、数据抽象、算法抽象来分析问题，应用适当的数据结构和算法来设计和实现相应的程序。通过课程学习，大家的抽象思维能力、问题求解能力将得到较大提 升，编程能力和代码质量会有质的飞跃！

在求解实际问题方面，我们会学习到通过权衡时空和其他资源开销，利用数据结构来组织数据、设计高效的算法、完成高质量的程序以满足错综复杂的实际应用需要。

此外，课程所学到的内容会被利用到计算机科学后续的各个课程中，如操作系统、软件工程、数据库概论、编译技术、计算机图形学、人机交互等。希望可以为大家将来从事计算机相关的学习、研究和开发工作打下扎实的基础。

本课程采用张铭主编的国家“十一五”规划教材《数据结构与算法》（高等教育出版社）。适合计算机以及相关理工专业的大二本科生学习，需要先修过计算概论等课程，具有结构化和面向对象的程序设计基础。

课程主要包括的内容有：线性表，栈与队列，字符串，二叉树，树，图，排序（内排序，外排序），检索，索引，高级数据结构、以及数据结构应用。课程持续16周（分为两个session，每个8周），学习者每周在本课程上需要投入4－8小时。

本课程的本次开设得到Google研究经费支持。

Computers are an important tool for problem solving and are deeply involved in modern life. Computers perform operations on data. What is the logical relationship among data? How is data stored in computers? What algorithms are required to solve particular problems? These are the questions that will be answered in “Data Structures and Algorithms,” an important core course in Computer Science. The course also introduces students to fundamental data structures and classical algorithms used in more specialized courses, including Operating Systems, Software Engineering, Database Systems, Compiler Principles, Computer Graphics and Human Computer Interaction.

Niklaus Wirth described the important and indivisible link between algorithms and data structure in his book, Algorithms + Data Structures = Programs.

The course will build on Wirth’s ideas as it helps students improve their knowledge of theory and their ability to think abstractly to solve problems. Building on a solid theoretical foundation, students will analyze problems using data and algorithm abstraction. Students will learn how to organize data efficiently and make tradeoffs between space and time complexity, design efficient and effective algorithms, and implement high quality programs to solve complex real-world problems. After studying this course, students will be well prepared for further study and research in engineering and other computer-related areas.

This is an intermediate-level course appropriate for sophomore students majoring in computer science or other science/engineering disciplines. Students should have learned "introduction to computing", with the knowledge of structured and object-oriented programming.

This course is presented in two eight-week sessions.

Students who score 60% or higher will receive an Honor Code Certificate.

The Autumn 2014 Sessions of this course are supported by Google.

课程采用的算法语言? Which programming languages does the course use?

本课程采用基于C++的伪代码授课和出习题。编程作业是POJ自动评判的，该平台目前接受 C、C++、Java等都可以。

The course’s content and exercises are both based upon C++ pseudo code. Programming assignments are automatically assessed by POJ which accepts code written in C/C++ and JAVA.

课程介绍视频也可以访问中国网站

计算机是现代社会中用于解决问题的重要工具。利用计算机解决实际问题需要将问题抽象，并对数据进行操作，最后通过计算机程序求解问题。而本门课程主要内容就是对以上内容进行研究。

图灵奖获得者N.Wirth写了一本经典著作“程序=算法+数据结构”。数据结构，是抽象的表示数据的方式；算法，则是计算的一系列有效、通用的步骤。算法与数据结构是程序设计中相辅相成的两个方面。

我 们会围绕着“算法+数据结构=程序”的思路，以问题求解为导向进行学习。希望能够帮助大家提高理论、抽象、设计的能力。在扎实的经典理论基础上，运用问题 抽象、数据抽象、算法抽象来分析问题，应用适当的数据结构和算法来设计和实现相应的程序。通过课程学习，大家的抽象思维能力、问题求解能力将得到较大提 升，编程能力和代码质量会有质的飞跃！

在求解实际问题方面，我们会学习到通过权衡时空和其他资源开销，利用数据结构来组织数据、设计高效的算法、完成高质量的程序以满足错综复杂的实际应用需要。

此外，课程所学到的内容会被利用到计算机科学后续的各个课程中，如操作系统、软件工程、数据库概论、编译技术、计算机图形学、人机交互等。希望可以为大家将来从事计算机相关的学习、研究和开发工作打下扎实的基础。

本课程采用张铭主编的国家“十一五”规划教材《数据结构与算法》（高等教育出版社）。适合计算机以及相关理工专业的大二本科生学习，需要先修过计算概论等课程，具有结构化和面向对象的程序设计基础。

在 第一部分学完了线性表、栈与队列、字符串、二叉树、树和图这些基础数据结构之后，第二部分我们将深入学习排序、检索、索引、高级数据结构以及数据结构应用 等内容。涉及快速排序、外排序等各种经典排序算法，集合、散列、位图等检索方法，B/B+树、Trie树等索引结构，广义表、多维数组等高级线性结 构，AVL、红黑树、伸展树等平衡二叉树。第二部分课程持续8周，学习者每周在本课程上需要投入4－8小时。本课程的本次开设得到Google研究经费支 持。

Computers are an important tool for problem solving and are deeply involved in modern life. Computers perform operations on data. What is the logical relationship among data? How is data stored in computers? What algorithms are required to solve particular problems? These are the questions that will be answered in “Data Structures and Algorithms,” an important core course in Computer Science. The course also introduces students to fundamental data structures and classical algorithms used in more specialized courses, including Operating Systems, Software Engineering, Database Systems, Compiler Principles, Computer Graphics and Human Computer Interaction.

Niklaus Wirth described the important and indivisible link between algorithms and data structure in his book, Algorithms + Data Structures = Programs.

The course will build on Wirth’s ideas as it helps students improve their knowledge of theory and their ability to think abstractly to solve problems. Building on a solid theoretical foundation, students will analyze problems using data and algorithm abstraction. Students will learn how to organize data efficiently and make tradeoffs between space and time complexity, design efficient and effective algorithms, and implement high quality programs to solve complex real-world problems. After studying this course, students will be well prepared for further study and research in engineering and other computer-related areas.

This is an intermediate-level course appropriate for sophomore students majoring in computer science or other science/engineering disciplines. Students should have learned "introduction to computing", with the knowledge of structured and object-oriented programming.

This course is presented in two eight-week sessions. In session 1, we learnt Linear Lists, Stacks, Queues, Strings, Binary Trees, Trees and Graphs, which are fundamental data structures. In the second session, we will study advanced data structures and algorithms, such as Sorting, Searching, Indexing, as well as their applications thoroughly. More detailed, these chapters include a variety of classic Sorting algorithms (Quicksort, External Sorting), Searching methods (Sets, Hash Tables, Bitmaps), Indexing structures (B/B+ trees, Trie trees), Advanced List-Structure (generalized lists, Multi-dimensional arrays) and Balanced Binary Trees (AVL, Red-Black trees, Splay trees). The second part of the course lasts eight weeks. Each week, student will spend 4-8 hours to follow this course.

Students who score 60% or higher will receive an Honor Code Certificate.

The Autumn 2014 Sessions of this course are supported by Google.

课程采用的算法语言? Which programming languages does the course use?

本课程采用基于C++的伪代码授课和出习题。编程作业是POJ自动评判的，该平台目前接受 C、C++、Java等都可以。

The course’s content and exercises are both based upon C++ pseudo code. Programming assignments are automatically assessed by POJ which accepts code written in C/C++ and JAVA.