Early 2014

Si alguna vez has tenido problemas con la estadística, este curso está hecho para ti. Es ideal para investigadores y alumnos que se encuentran cursando trabajos fin de grado, trabajos fin de máster o realizando la tesis y que quieren realizar un análisis cuantitativo en sus estudios.

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June 2014

An introduction to statistical ideas and methods commonly used to make valid conclusions based on data from random samples.

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Can we program machines to learn like humans? This Reinforcement Learning course will teach you the algorithms for designing self-learning agents like us!

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May 12th 2014

Learn to frame and address health-related questions using modern biostatistics ideas and methods.

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May 5th 2014

Learn the concepts and tools behind reporting modern data analyses in a reproducible manner.

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May 5th 2014

R es un entorno informático de computación estadística y de generación de gráficos. R funciona en un amplio rango de sistemas operativos como UNIX, Windows o MacOS. Pese a su potencialidad, versatilidad y flexibilidad; R puede parecer árido en el momento en que el usuario trata de interaccionar con sus componentes. Se suele decir que “la curva de aprendizaje es lenta”. Sin embargo, los resultados que produce son ampliamente satisfactorios. Este curso está destinado a “lubricar” esos primeros encuentros con éste entorno estadístico.

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May 5th 2014

Learn how to draw conclusions about populations or scientific truths from data.

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Apr 30th 2014

Learn how advances in geospatial technology and analytical methods have changed how we do everything, and discover how to make maps and analyze geographic patterns using the latest tools.

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Apr 28th 2014

This course follows on from Data Mining with Weka and provides a deeper account of data mining tools and techniques. Again the emphasis is on principles and practical data mining using Weka, rather than mathematical theory or advanced details of particular algorithms.

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Apr 28th 2014

How astronomy really works - an overview of the technology that astronomers use to collect and measure light from the universe, and how it is used in practice to make scientific discoveries.

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Apr 21st 2014

Learn both theory and application for basic methods that have been invented either for developing new concepts – principal components or clusters, or for finding interesting correlations – regression and classification. This is preceded by a thorough analysis of 1D and 2D data.

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Apr 14th 2014

An introduction to probability, with the aim of developing probabilistic intuition as well as techniques needed to analyze simple random samples.

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Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning!

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Apr 7th 2014

Get an overview of the data, questions, and tools that data analysts and data scientists work with. This is the first course in the Johns Hopkins Data Science Specialization.

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Apr 7th 2014

Learn how to program in R and how to use R for effective data analysis. This is the second course in the Johns Hopkins Data Science Specialization.

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Apr 7th 2014

Learn how to gather and clean data from a variety of sources. This is the third course in the Johns Hopkins Data Science Course Track.

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Apr 7th 2014

Data Analysis for Genomics will teach students how to harness the wealth of genomics data arising from new technologies, such as microarrays and next generation sequencing, in order to answer biological questions, both for basic cell biology and clinical applications.

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