Coursera

Every dog owner should have some understanding of reproduction, whether they intend to breed that dog or just want to have their dog spayed or castrated. As a dog enthusiast, you want to know what is best for your dog and to understand your veterinarian’s recommendations. This course is intended to give you the background knowledge necessary to help you achieve those goals.

We will introduce methods to perform systematic reviews and meta-analysis of clinical trials. We will cover how to formulate an answerable research question, define inclusion and exclusion criteria, search for the evidence, extract data, assess the risk of bias in clinical trials, and perform a meta-analysis.

Clinical trials are experiments designed to evaluate new interventions to prevent or treat disease in humans. The interventions evaluated can be drugs, devices (e.g., hearing aid), surgeries, behavioral interventions (e.g., smoking cessation program), community health programs (e.g. cancer screening programs) or health delivery systems (e.g., special care units for hospital admissions). We consider clinical trials experiments because the investigators rather than the patients or their doctors select the treatment the patients receive. Results from randomized clinical trials are usually considered the highest level of evidence for determining whether a treatment is effective because trials incorporates features to ensure that evaluation of the benefits and risks of treatments are objective and unbiased. The FDA requires that drugs or biologics (e.g., vaccines) are shown to be effective in clinical trials before they can be sold in the US.

Learn How To Develop Open Source Software. Get the skills and knowledge you need to develop open source software using Linux, git, and more!

Master Linux and Cloud Computing. Learn how enterprises use and administer Linux in a private cloud environment.

Ace the IELTS Test. Use effective strategies for increasing your band scores on the IELTS exam.

Beginner to Programmer — Learn to Code in C & C++. Gain a deep understanding of computer programming by learning to code, debug, and solve complex problems with C and C++.

The main purpose of this course is to focus on good questions and how to answer them. This is essential to making considered decisions as a leader in any organization or in your life overall. Topics will include the basis of human curiosity, development of questions, connections between questions and approaches to information gathering design , variable measurement, sampling, the differences between experimental and non-experimental designs, data analysis, reporting and the ethics of inquiry projects. Emphasis will be placed on approaches used in the social sciences (i.e., sociology, psychology, anthropology), but we will also discuss how others (i.e., natural scientists, business analysts) might approach the inquiry process. No prior knowledge of statistics is required for this course.

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

If you’ve ever skipped over`the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place. You may be a clinical practitioner reading research articles to keep up-to-date with developments in your field or a medical student wondering how to approach your own research. Greater confidence in understanding statistical analysis and the results can benefit both working professionals and those undertaking research themselves.