HarvardX

Description 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…

Description 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…

Description This course bridges the gap between introductory and advanced courses in Python. While there are many excellent introductory Python courses available, most typically do not go deep enough for you to apply your Python skills to research projects. In this course, after first reviewing the basics of Python 3, we learn about tools commonly…

Description Improving access to healthcare is only as useful as the quality of care provided. Many agree that quality is important – but what is it? How do we define it? How do we measure it? And most importantly, how might we make it better?The course is designed for those who care about health and…

Description From the Syrian refugee crisis to the West Africa Ebola outbreak, humanitarian emergencies have reached unprecedented dimensions and proportions. As need for humanitarian aid grows, how can efforts to alleviate human suffering evolve with it? This course from the Harvard Humanitarian Initiative and HarvardX seeks to prepare learners to recognize and analyze emerging challenges…

Description Causal diagrams have revolutionized the way in which researchers ask: What is the causal effect of X on Y? They have become a key tool for researchers who study the effects of treatments, exposures, and policies. By summarizing and communicating assumptions about the causal structure of a problem, causal diagrams have helped clarify apparent…

Description The weather forecasts we see every day are based on an army of meteorological sensing networks and intensive computer modeling. Before the rise of these technologies, predictions were made by methods like discerning cloud formations and wind directions. This course will explore the science behind weather systems by teaching the observational skills needed to…

Description In 1854, a cholera epidemic swept through the London neighborhood of Soho. In the course of about three weeks, over 600 people died. This incident was, tragically, not unusual in London or the rest of the 19th century world as a whole. The scourge of cholera seemed unstoppable and, even worse, unpredictable. But one…

Description In this course, part of our Professional Certificate Program in Data Science,we cover several standard steps of the data wrangling process like importing data into R, tidying data, string processing, HTML parsing, working with dates and times, and text mining. Rarely are all these wrangling steps necessary in a single analysis, but a data…

Description Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding. This course, part ofourProfessional Certificate Program in Data Science, covers how to implement linear regression and adjust for confounding in practice using R. In data science applications, it is very common to…