Online courses directory (10358)

Many health complaints, such as cardiovascular disease, diabetes or joint problems seem inescapable diseases of old-age, but originate mostly from our lifestyle. A lifestyle that is largely dictated by our environment. The mismatch between our 'old' genes - our evolutionary heritage - and the 'modern' environment is the reason that we cannot resist the constant stimuli that seduce us to make unhealthy choices. Do's and don'ts cannot help us. 

This health course will take a radically different approach to showing you how to secure a lifestyle, which will keep you healthy throughout the lifespan. We have to let the environment do the work for us. By making small changes at our home, work, school or neighbourhood, healthier choices can become easier and automated.

Practical assignments will give you the skills to re-design your daily environments to promote healthy, longer lives for you and your family, colleagues and neighbours with whom you share your re-designed environments. You will learn how to create your own healthy living zone!

We will take you on excursions to Copenhagen, Denmark and to Ghent, Belgium to show you hands-on how the environment can help you live longer, healthier and happier lives!

This course is brought to you by:

Video image ©Tijs Koelemeijer / info@tijskoelemeijer.com

This class examines the built, psychosocial, economic, and natural environment factors that affect health behaviors and outcomes. Students will be introduced to tools designed to integrate public health considerations into policy making and planning, and will be given hands-on training on the application of Health Impact Assessment (HIA) methodology. This class is designed to prepare graduate students from planning and policy fields to interface with public health organizations, agencies, or advocacy groups in professional contexts.

<p><strong></strong><span style="color: rgb(0, 0, 0);">This free online course is all about healthy living. It is intended to make you more aware of what you eat and drink. This course will show you how to exercise and create a balanced diet that can help you to stay fit and healthy. It will help you to follow a healthy lifestyle by choosing foods that are appropriate for you and this will help you to plan your exercise regime. It will also explain calories and food energy. It will make you think about the dangers of alcohol. This course will help you to understand food labels and help you put healthy eating into practice.</span><span style="font-weight: bold; color: rgb(255, 0, 0);"><br /></span></p> <p><span style="font-weight: bold; color: rgb(255, 0, 0);">Note</span>: This course has been optimised for Internet Explorer, some features may not function or display correctly in other Web browsers such as Firefox. </p> <p></p>

Learn more about how your heart works, the causes of heart disease and what you can do to keep your heart healthy.

learn about desktop support or IT Support.

A bunch of video tutorials to make GCSE Maths a whole lot easier. This course is pitched at the D/C borderline.

As an adult trainer, are you sick of the “Death by Powerpoint” style? Are you thinking of using different ways to engage your learners? Do you toss and turn at night wondering if your materials have been effective and useful for your learners? Enroll in this short course to pick up some tips on using a range of instructional methods to engage your adult learners and make their learning relevant and meaningful.

This course examines major works by Hemingway, Fitzgerald, and Faulkner, exploring their interconnections on three analytic scales: the macro history of the United States and the world; the formal and stylistic innovations of modernism; and the small details of sensory input and psychic life.

Warning: Some of the lectures in this course contain graphic content and/or adult language that some users may find disturbing.

Aprende paso a paso como utilizar cada una de las herramientas del programa Photoshop CC y editar tus imágenes.

This course introduces concepts, languages, techniques, and patterns for programming heterogeneous, massively parallel processors. Its contents and structure have been significantly revised based on the experience gained from its initial offering in 2012. It covers heterogeneous computing architectures, data-parallel programming models, techniques for memory bandwidth management, and parallel algorithm patterns.

This class is offered as CS6290 at Georgia Tech where it is a part of the [Online Masters Degree (OMS)](http://www.omscs.gatech.edu/). Taking this course here will not earn credit towards the OMS degree. The course begins with a lesson on performance measurement, which leads to a discussion on the necessity of performance improvement. Pipelining, the first level of performance refinement, is reviewed. The weaknesses of pipelining will be exposed and explored, and various solutions to these issues will be studied. The student will learn hardware, software, and compiler based solutions to these issues.

The goal of this course is to give you solid foundations for developing, analyzing, and implementing parallel and locality-efficient algorithms. This course focuses on theoretical underpinnings. To give a practical feeling for how algorithms map to and behave on real systems, we will supplement algorithmic theory with hands-on exercises on modern HPC systems, such as Cilk Plus or OpenMP on shared memory nodes, CUDA for graphics co-processors (GPUs), and MPI and PGAS models for distributed memory systems. This course is a graduate-level introduction to scalable parallel algorithms. “Scale” really refers to two things: efficient as the problem size grows, and efficient as the system size (measured in numbers of cores or compute nodes) grows. To really scale your algorithm in both of these senses, you need to be smart about reducing asymptotic complexity the way you’ve done for sequential algorithms since CS 101; but you also need to think about reducing communication and data movement. This course is about the basic algorithmic techniques you’ll need to do so. The techniques you’ll encounter covers the main algorithm design and analysis ideas for three major classes of machines: for multicore and many core shared memory machines, via the work-span model; for distributed memory machines like clusters and supercomputers, via network models; and for sequential or parallel machines with deep memory hierarchies (e.g., caches). You will see these techniques applied to fundamental problems, like sorting, search on trees and graphs, and linear algebra, among others. The practical aspect of this course is implementing the algorithms and techniques you’ll learn to run on real parallel and distributed systems, so you can check whether what appears to work well in theory also translates into practice. (Programming models you’ll use include Cilk Plus, OpenMP, and MPI, and possibly others.)

Programming-oriented course on effectively using modern computers to solve scientific computing problems arising in the physical/engineering sciences and other fields. Provides an introduction to efficient serial and parallel computing using Fortran 90, OpenMP, MPI, and Python, and software development tools such as version control, Makefiles, and debugging.

6.776 covers circuit level design issues of high speed communication systems, with primary focus being placed on wireless and broadband data link applications. Specific circuit topics include transmission lines, high speed and low noise amplifiers, VCO's, mixers, power amps, high speed digital circuits, and frequency synthesizers. In addition to learning analysis skills for the above items, students will gain a significant amount of experience in simulating RF circuits in SPICE and also building RF circuits within a lab project.

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

Learn how to articulate your thoughts in a clear and concise manner that will allow your ideas to be better understood by your readers. Improve your business writing skill by learning to select and use appropriate formats for your audience, use the correct medium and adjust your writing style accordingly, as well as identify your objective and communicate it clearly.

The goal of this course is to review grammar and develop vocabulary building strategies to refine oral and written expression. Speaking and writing assignments are designed to expand communicative competence. Assignments are based on models and materials drawn from contemporary media (newspapers and magazines, television, Web). The models, materials, topics and assignments vary from semester to semester.

The goal of this course is to review grammar and develop vocabulary building strategies to refine oral and written expression. Speaking and writing assignments are designed to expand communicative competence. Assignments are based on models and materials drawn from contemporary media (newspapers and magazines, television, Web). The models, materials, topics and assignments vary from semester to semester.

The goal of this course is to review grammar and develop vocabulary building strategies to refine oral and written expression. Speaking and writing assignments are designed to expand communicative competence. Assignments are based on models and materials drawn from contemporary media (newspapers and magazines, television, Web). The models, materials, topics and assignments vary from semester to semester.

If you’re interested in data analysis and interpretation, then this is the data science course for you.

Enhanced throughput: Almost all recently manufactured laptops and desktops include multiple core CPUs. With R, it is very easy to obtain faster turnaround times for analyses by distributing tasks among the cores for concurrent execution. We will discuss how to use Bioconductor to simplify parallel computing for efficient, fault-tolerant, and reproducible high-performance analyses. This will be illustrated with common multicore architectures and Amazon’s EC2 infrastructure.  

Enhanced interactivity: New approaches to programming with R and Bioconductor allow researchers to use the web browser as a highly dynamic interface for data interrogation and visualization. We will discuss how to create interactive reports that enable us to move beyond static tables and one-off graphics so that our analysis outputs can be transformed and explored in real time.

Enhanced reproducibility: New methods of virtualization of software environments, exemplified by the Docker ecosystem, are useful for achieving reproducible distributed analyses. The Docker Hub includes a considerable number of container images useful for important Bioconductor-based workflows, and we will illustrate how to use and extend these for sharable and reproducible analysis.

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