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

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The Science of Decisions.

We live in a time of unprecedented access to information...data. Whether researching the best school, job, or relationship, the Internet has thrown open the doors to vast pools of data. Statistics are simply objective and systematic methods for describing and interpreting information so that you may make the most informed decisions about life.

More info: https://www.udacity.com/course/st095

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 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 31st 2016

Financial Engineering is a multidisciplinary field involving finance and economics, mathematics, statistics, engineering and computational methods. The emphasis of FE & RM Part II will be on the use of simple stochastic models to (i) solve portfolio optimization problems (ii) price derivative securities in various asset classes including equities and credit and (iii) consider some advanced applications of financial engineering including algorithmic trading and the pricing of real options. We will also consider the role that financial engineering played during the financial crisis.

Business
, Finance
, Statistics
, Risk
, Management
, Economics
, Math
, Engineering
, Financial Engineering
, Risk Management
, CAPM
, Mean Variance
, Black-Scholes

Oct 24th 2016

This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.

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.

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Self-Paced MOOCs

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Language

Type of Certificate

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