This course covers the design, acquisition, and analysis of Functional Magnetic Resonance Imaging (fMRI) data.

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The purpose of this course is to introduce you to the subject of statistics as a science of data. There is data abound in this information age; how to extract useful knowledge and gain a sound understanding in complex data sets has been more of a challenge. In this course, we will focus on the fundamentals of statistics, which may be broadly described as the techniques to collect, clarify, summarize, organize, analyze, and interpret numerical information.

This course will begin with a brief overview of the discipline of statistics and will then quickly focus on descriptive statistics, introducing graphical methods of describing data. You will learn about combinatorial probability and random distributions, the latter of which serves as the foundation for statistical inference. On the side of inference, we will focus on both estimation and hypothesis testing issues. We will also examine the techniques to study the relationship between two or more variables; this is known as regression.

By the end of this course, you should gain a sound understanding about what statistics represent, how to use statistics to organize and display data, and how to draw valid inferences based on data by using appropriate statistical tools.

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Oct 24th 2016

This course covers the design, acquisition, and analysis of Functional Magnetic Resonance Imaging (fMRI) data.

Oct 18th 2016

Use R to learn the fundamental statistical topic of basic inferential statistics. In the second part of a two part course, we’ll learn how to take data and use it to make reasonable and useful conclusions. You’ll learn the basics of statistical thinking – starting with an interesting question and some data.

Oct 24th 2016

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization.

Oct 24th 2016

We are always using experiments to improve our lives, our community, and our work. Are you doing it efficiently? Or are you (incorrectly) changing one thing at a time and hoping for the best? In this course, you will learn how to plan efficient experiments - testing with many variables. Our goal is to find the best results using only a few experiments. A key part of the course is how to optimize a system.

Oct 3rd 2016

This course is a hands-on introduction to statistical data analysis that emphasises fundamental concepts and practical skills.

Aug 29th 2016

Il corso copre la matematica di base, permettendo di colmare eventuali lacune e di mettere a punto la preparazione necessaria all'ingresso all'università.

The course covers the fundamentals of Math, thus allowing to fill high school gaps and to optimize students’ knowledge as they start college.

Sep 26th 2016

Our world is rich with data sources, and technology makes data more accessible than ever before! To help ensure students are future ready to use data for making informed decisions, many countries around the world have increased the emphasis on statistics and data analysis in school curriculum–from elementary/primary grades through college. This course allows you to learn, along with colleagues from other schools, an investigation cycle to teach statistics and to help students explore data to make evidence-based claims.

Oct 10th 2016

In this course you will learn a whole lot of modern physics (classical and quantum) from basic computer programs that you will download, generalize, or write from scratch, discuss, and then hand in. Join in if you are curious (but not necessarily knowledgeable) about algorithms, and about the deep insights into science that you can obtain by the algorithmic approach.

Oct 31st 2016

By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials.

Oct 24th 2016

Inferential statistics are concerned with making inferences based on relations found in the sample, to relations in the population. Inferential statistics help us decide, for example, whether the differences between groups that we see in our data are strong enough to provide support for our hypothesis that group differences exist in general, in the entire population.

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