Online courses directory (19947)

The actions of ordinary people are often absent in studies of urban renewal and urban ecology. Around the world, people who are fed up with environmental degradation and the breakdown of their communities come together to transform blighted vacant lots, trashed-out stream corridors, polluted estuaries, and other “broken places.” Civic ecology practices—such as community gardening, wetlands restoration, river cleanups, and tree planting—are a means for people to express resilience and rebuild communities marked by disaster and disinvestment.

Civic ecology draws on psychology, sociology, political science, education, ecology, and social-ecological systems resilience to understand how and why people care for nature and their communities.

Throughout this course, you will:

• Explore the people, places, and practices that restore nature and revitalize neighborhoods, making a difference in ways big and small.
• Discuss and evaluate contemporary thinking in resilience, social-ecological systems, and the relationship between nature and human/community wellbeing.
• Grasp an understanding of how civic ecology enables those with limited resources to defy and cope with daily struggles, including after disaster and war.
• Acquire the knowledge and skill set to enact change in your own community.
• Participate in a civic ecology service learning project to turn classroom learning into real-life application.

This course is ideal for a learner who is intrigued by both social and environmental concerns, or simply has a desire to dive into an emerging 21st century, cross-disciplinary subject area. You will complete this course with a keener awareness of social-ecological issues and concerns, as well as a greater knowledge of the practical steps required to rebuild and maintain community and nature in a world marked by inequality, conflict, and climate change.

Cuando hacer las cosas bien no es suficiente, necesitamos convertir la mejora continua en un lema diario para todos los trabajadores de la empresa. Para ello necesitamos conocer técnicas que nos ayuden a realizar esta tarea de manera más sistemática para que, por un lado tengamos mejores resultados y, por otro, no nos dispersemos con las urgencias de un día a día caótico. En este curso, aprenderás a definir problemas/oportunidades, y a convertirlas en un proyecto de mejora.

Descubrirás cómo construir un equipo que se haga cargo de estos proyectos y conocerás y practicaras diferentes técnicas para la resolución de problemas en grupo. Al finalizar el curso serás capaz de participar en grupos de resolución de problemas quesean capaces de mejorar la eficiencia de las organizaciones.

The modern smartphone is enabled by a billion-plus nanotransistors, each having an active region that is barely a few hundred atoms long. Interestingly the same amazing technology has also led to a deeper understanding of the nature of current flow on an atomic scale and my aim is to make these lessons from nanoelectronics accessible to anyone in any branch of science or engineering. I will assume very little background beyond linear algebra and differential equations, although we will be discussing advanced concepts in non-equilibrium statistical mechanics that should be of interest even to specialists.
 
In the first half of this course (4 weeks) we will introduce a new perspective connecting the quantized conductance of short ballistic conductors to the familiar Ohm's law of long diffusive conductors, along with a brief description of the modern nanotransistor. In the second half (4 weeks) we will address fundamental conceptual issues related to the meaning of resistance on an atomic scale, the interconversion of electricity and heat, the second law of thermodynamics and the fuel value of information.
 
Overall I hope to show that the lessons of nanoelectronics lead naturally to a new viewpoint, one that changes even some basic concepts we all learn in freshman physics. This unique viewpoint not only clarifies many old questions but also provides a powerful approach to new questions at the frontier of modern nanoelectronics, such as how devices can be built to control the spin of electrons.
 
This course was originally offered in 2012 on nanoHUB-U and the accompanying text was subsequently published by World Scientific. I am preparing a second edition for publication in 2015, which will be used for this course. The manuscript will be made available to registered students.
 
Sample comments:
From Roald Hoffmann, http://en.wikipedia.org/wiki/Roald_Hoffmann
Cornell University
 "… the pedagogical imperative in research is very important to me, and so I really value a kindred spirit. Your (Datta's) online courses are just wonderful!"
 
From anonymous student in previous offering.
"The course was just awesome .. Prof. Datta's style of delivering lecture is mind-blowing."
 
This course is the latest in a series offered by the nanoHUB-U project which is jointly funded by Purdue and NSF with the goal of transcending disciplines through short courses accessible to students in any branch of science or engineering. These courses focus on cutting-edge topics distilled into short lectures with quizzes and practice exams.

Imaging technologies form a significant component of the health budgets of all developed economies, and most people need advanced imaging such as MRIs, X-Rays and CT Scans (or CAT Scans) during their life. Many of us are aware of the misinformation sometimes offered in TV dramas, which either exaggerates the benefits or overemphasizes the risks.

This medical imaging course provides an introduction to biomedical imaging and modern imaging modalities. The course also covers the basic scientific principals behind each modality, and introduces some of the key applications, from neurological diseases to cancers. This course includes modules specially designed for the general public, whilst also providing some advanced modules which could contribute to professional development in health, engineering and IT industries.

If you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction and multi-dimensional scaling and its connection to principle component analysis. We will learn about the batch effect: the most challenging data analytical problem in genomics today and describe how the techniques can be used to detect and adjust for batch effects. Specifically, we will describe the principal component analysis and factor analysis and demonstrate how these concepts are applied to data visualization and data analysis of high-throughput experimental data.

Finally, we give a brief introduction to machine learning and apply it to high-throughput data. We describe the general idea behind clustering analysis and descript K-means and hierarchical clustering and demonstrate how these are used in genomics and describe prediction algorithms such as k-nearest neighbors along with the concepts of training sets, test sets, error rates and cross-validation.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

PH525.2x: Introduction to Linear Models and Matrix Algebra

PH525.3x: Statistical Inference and Modeling for High-throughput Experiments

PH525.4x: High-Dimensional Data Analysis

PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays 

PH525.6x: High-performance computing for reproducible genomics

PH525.7x: Case studies in functional genomics

Intro to Animation. Incrementing Shortcuts. If Statements.

Intro to Drawing. Challenge: H for Hopper. More Drawing!. Challenge: Simple Shapes!. Challenge: CRAZY Face. Intro to Coloring. Challenge: Ice Cream Code. Challenge: It's a Beautiful Day. Intro to Variables. Challenge: Bucktooth Bunny. More on Variables. Challenge: Funky Frog. Variable Expressions. Intro to Animation. Mouse Interaction. Incrementing Shortcuts. Using Math Expressions. Terrific Text: Part One. Terrific Text: Part Two.

If Statements. More Mouse Interaction. Booleans. If/Else - Part 1. If/Else - Part 2.

This course is offered in an experimental format. Students are welcome to audit the course, and participate in all course activities. Certificates will not be issued.

Many early childhood educators report feeling ill equipped to meet the needs of children with challenging behavior and frustrated in their attempts to develop safe and nurturing early learning environment. If you work with young children, you are not alone in your feelings! Increasing evidence suggests that an effective approach to addressing problem behavior is the adoption of a model that focuses on promoting social-emotional development, providing support for children’s appropriate behavior, and preventing challenging behavior. In this class, you will learn a framework for addressing the social and emotional development and challenging behavior of young children.

The overarching goal of this course is to learn evidence-based practices to support the social and emotional development of infants and young children. We will read current research on the developmental trajectory of children with early-onset aggressive behaviors; positive behavior support program models; and intervention efforts that promote positive early childhood mental health. Evidence-based classroom management skills will also be studied and you will leave the course with a solid understanding of how to design supportive environments. This course incorporates a community with which you can learn from others, share your own current approaches and discuss your attempts to incorporate the learnings of this course into your early childhood practice.

Android es la plataforma libre desarrollada por Google, ampliamente utilizada en multitud de dispositivos como móviles, tabletas, TV, wearables e Internet de las cosas. Su expansión ha sido espectacular, siendo el S.O. más utilizado en la actualidad. Tras realizar este curso conocerás los fundamentos del desarrollo de aplicaciones en Android y podrás realizar sencillas aplicaciones, que incluyan los aspectos más importantes y novedosos de esta plataforma.

A lo largo del curso se desarrolla una aplicación de ejemplo, "Mis Lugares Favoritos", que nos permitirá almacenar fotografía, posición geográfica, valoración y otros datos de los lugares que más nos gustan. El curso se introducen los siguientes aspectos: repaso de Java, visión general de Android, entorno de desarrollo (Android Studio), Interfaz de usuario (vistas, Layouts, recursos, barra de acciones, preferencias, RecyclerView, Fragments...), actividades, intenciones, seguridad, posicionamiento, mapas y bases de datos.

In this education and teacher training course we will explore effective teaching methods for biology. We will emphasize approaches proven to be effective and show you how to implement them. We will also give you the opportunity to reflect on your own teaching experience and exchange ideas and share challenges with other learners in the course.

We will begin by looking at the most common method of teaching science, the lecture. We’ll discuss what the lecture method does well and look at data that illustrates when it is less effective. You will hear highly successful teachers talk about their experience with lecture and how they modified their lecture time to more actively engage students. We’ll investigate creating learning objectives and how they can be used to communicate your expectations to students. You will practice writing your own learning objectives and see how they can streamline exam construction. We’ll look at a variety of ways to include active learning during class time, discuss how active learning strategies support your learning objectives, and give you practice developing learning activities for biology topics you find challenging to teach. 

Lastly, we’ll look at how to use resources for student learning outside of class, and how to know that your students have successfully learned from both in-class and outside of class activities.

Our course is designed for instructors, or instructors-to-be, of undergraduate-level biology. High school instructors of AP Biology, post-doctoral fellows and graduate students looking ahead to teaching should find the course useful. The course can serve as a means of professional development. There are no pre-requisites, although prior satisfactory completion of a college biology course is highly recommended.

What impact can art and the humanities have on patient care? Can art enhance medical caregivers’ powers of observation and humanize their interactions with patients? Through presence alone, can caregivers heal what they cannot cure?

In Artful Medicine: Art’s Power to Enrich Patient Care, we will explore the meaning of medical professionalism, techniques for a humanistic approach to caregiving, and the positive impact of humanism on clinical outcomes.

By looking closely at works of art that portray empathy in human interaction, caregivers can discover their common humanity with patients. Caregivers can also hone observation skills that help paint a portrait of a patient as a person and not a collection of symptoms, lab tests, and scans. Through presence at the patient’s side, unmediated by technology, a caregiver can make the patient feel cared for in addition to being treated.

One needn’t be a caregiver to benefit from this course—though all of us have probably been cared for at some point and can therefore easily relate to the importance of a humanistic approach to caregiving. Anyone interested in how art can be used to enhance observational and empathic skills will find this application to medical humanism fascinating.

We will:

• Explore elements of professionalism and humanism, why they are critical to patient care, and how they are threatened by technology that increasingly distances caregivers from patients;
• Analyze art for form, narrative, and technique;
• Practice techniques used by art educators to enhance observation and improve diagnostic skills;
• Examine works of art for the professional behaviors they exemplify;
• Apply techniques to patient care that will help humanize interactions, so caregivers can refocus their attention on patients and families and enhance their understanding of behaviors critical to healing; and
• Identify with both the caregiver’s and the patient’s points of view.

This MOOC is inspired by residential seminar taught by Dr. Schiffman at Brown University's Warren Alpert School of Medicine under the name The Doc's HeArt: Reflecting on Professional Values Through Art, and is especially relevant for healthcare professionals who are interested in differentiating themselves in their given or prospective field.

The field of learning games is rapidly growing, with interest from academics, publishers, schools and startups. But what makes a good learning game? Where do ideas come from and how do you create them? These are the questions that this course tries to answer.

The premise of learning games from some perspectives seems like a perfect and easy solution - get people to learn things they don’t want to by motivating them through game play. To others it seems like an oxymoron - if learning is hard then it can’t possibly be fun at the same time. The key to designing good learning games is to reconcile these perspectives by creating games that focus on creating environments based on “hard fun.” This idea will be a central tenet of this course.

Through six units comprising nine weeks, we will look at the evolution of educational video games and hear from experts working on many aspects of learning games from design to development to implementation. For the course project, participants will create their own learning game. This course utilizes Gameblox, a game editor that uses a block based programming language to allow anyone to create games.

This course is part of the Microsoft Professional Program Certificate in Big Data and the Microsoft Professional Program Certificate in Data Science.

Are you ready for big data science? In this course, learn how to implement predictive analytics solutions for big data using Apache Spark in Microsoft Azure HDInsight. See how to work with Scala or Python to cleanse and transform data and build machine learning models with Spark ML (the machine learning library in Spark),

Note: To complete the hands-on elements in this course, you will require an Azure subscription and a Windows client computer. You can sign up for a free Azure trial subscription (a valid credit card is required for verification, but you will not be charged for Azure services). Note that the free trial is not available in all regions.

This course is also part of the Microsoft Azure HDInsight Big Data Analyst XSeries.

Have you ever imagined what is deep under the ground? What is happening deep inside the earth? How has the earth evolved into its present state? This course is an introduction to earth science, focusing on the deep earth. We will learn how temperature and chemical compositions inside the Earth are inferred from limited observations combined with laboratory experiments. We will also explore the fate of water on the early Earth related to advanced research questions. Upon finishing this course, you will learn how scientists interpret the unknown and use the scientific method to address immeasurable research challenges.

No specific knowledge is needed. Join this course and let’s imagine the inside of the Earth together.

Urban school reform in the United States is characterized by contentious, politicized debate. This course explores a set of critical issues in the education and educational reform space, with a focus on aspects of the field that have sparked controversy and polarized views. We will dig into these debates, situating them within the larger history of public education and school reform, and considering the viewpoints, the evidence, and translation of issues into educational policy.
 
The class is designed with multiple student perspectives in mind with appropriate content and access points for policymakers, school leaders, teachers and parents or other concerned citizens. No background knowledge is required.
 
We will consider three themes in this course:

1. Federal Strategies in School Reform: How has the federal government legislated and incented public school reform? What are the implications of those approaches given the nature of local control in American public education? We will discuss three particular strategies the federal government has enacted recently and the diverging perspectives on them.
2. School Choice: How does school choice aim to improve schools? What forms does it take? Does providing school choice improve schools?
3. Accountability: What is the history of accountability in American public schooling? What are the policies and practices associated with accountability and what are the assumptions behind them? Does accountability lead to improved outcomes for students?

Principles of Electric Circuits (20220214x) is one of the kernel courses in the broad EECS subjects. Almost all the required courses in EECS are based on the concepts learned in this course, so it’s the gateway to a qualified EECS engineer.

The main content of this course contains linear and nonlinear resistive circuits, time domain analysis of the dynamic circuits, and the steady state analysis of the dynamic circuits with sinusoidal excitations. Important concepts, e.g. filters, resonance, quiescent point, etc., cutting-edge elements, e.g. MOSFETs and Op Amps, etc., systematic analyzing tools, e.g. node method and phasor method, etc., and real-world engineering applications, e.g. square wave generator and pulse power supply for railgun, etc., will be discussed in depth.

In order to facilitate the learning for students with middle school level, we prepare the necessary knowledge for calculus and linear algebra in week 0. With your effort, we can show you the fantastic view of electricity.

电路原理课程是电类各专业最重要的一门学科基础课，后续各专业基础课和专业课都建立在这门课程的知识体系之上，因此是电类专业本科生的“看家 课”之一。电路原理课程的主要内容包括：线性电阻电路分析、非线性电阻电路分析、动态电路的时域分析和正弦激励下动态电路的稳态分析4大部分。清华大学电 路原理课程的教学包括电路分析基本方法、当代电路元器件、电路原理的实际工程应用等，为学生提供了扎实的基础和丰富的应用。

为方便至只有中学知识的学生学习，电路原理MOOC专门利用第0周准备必要的微积分、线性代数和大学物理电学的基础。我们有信心：你能领悟电世界的奇妙。    

¿Quieres ser capaz de valorar de forma analítica a Cristiano Ronaldo o a Fernando Torres usando el Proceso Analítico Jerárquico (AHP), método empleado a nivel mundial para la valoración de todo tipo de activos?

El mundo del deporte mueve miles de millones de euros, muchos de los cuales se invierten en los traspasos de deportistas entre equipos. La valoración de futbolistas es tema complejo en el que influyen muchas variables con relaciones complejas.

En el curso aprenderás a utilizar herramientas que permiten seleccionar de una forma objetiva el mejor jugador para una posición dada o estimar el valor del traspaso de un jugador utilizando las cantidades pagadas por jugadores similares en operaciones recientes.

El contenido del curso es el siguiente:

Unidad 1: El mundo del deporte y la valoración
Unidad 2: El proceso analítico jerárquico (AHP)
Unidad 3: Aplicación de AHP a la valoración de deportistas
Unidad 4: Ejemplos

Este curso está actualmente en modo autónomo o “self-paced”. ¿Qué significa esto? Que puedes empezarlo cuando quieras y seguirlo a tu ritmo ya que no hay fecha prevista de cierre y cada 6-8 semanas se generarán certificados a aquellos que lo hayan superado. Por otro lado los profesores participarán algo menos en los foros, seguirás teniendo soporte por su parte pero es posible que tarde algo más en contestar tus dudas.

This course is taught in Spanish with English subtitles.

In this course, we will introduce you to edX Studio, edX’s course-authoring tool. This course is ideal for course authors and course teams interested in uncovering the nuts and bolts of building an edX course. We will cover everything you need to know to successfully create your first course on the edX platform, including:

• The basics of course set-up
• Adding course content, including videos, assessments, and interactive components
• Configuring course settings and optimizing the course experience for learners

Through engaging activities and hands-on learning, this course will walk you through the course development process directly in Studio.

Important note: Access to an updated version of the edX platform is required to complete this course. If you are a member of one of edX’s partner institutions and are interested in creating a course with edX, please reach out to your institutional leadership or contact your edX Program Manager.

In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation. We provide several examples of how these concepts are applied in next generation sequencing and microarray data. Finally, we will discuss hierarchical models and empirical bayes along with some examples of how these are used in practice. We provide R programming examples in a way that will help make the connection between concepts and implementation.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

PH525.2x: Introduction to Linear Models and Matrix Algebra

PH525.3x: Statistical Inference and Modeling for High-throughput Experiments

PH525.4x: High-Dimensional Data Analysis

PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays 

PH525.6x: High-performance computing for reproducible genomics

PH525.7x: Case studies in functional genomics