Data Science

 

 


 

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E.g., 2017-08-22
E.g., 2017-08-22
E.g., 2017-08-22
Nov 1st 2017

Once, only cartographers made maps. Today anyone can. Still, cartographers can teach people to make better maps, just as chefs can show people how to cook better meals. With coaching from experienced cartographers and practical, hands-on exercises using ArcGIS Pro, you'll become a smarter mapmaker, ready to go beyond the defaults and make better maps.

Average: 2 (1 vote)
Sep 25th 2017

Among its many evolutions, the Web became a way to exchange data between applications. Everyday we consume and produce these data through a growing variety of applications running on a growing variety of devices. This major evolution of the Web has applications in all domains of activity. This MOOC introduces the Linked Data standards and principles that provide the foundation of the Semantic web.

Average: 10 (1 vote)
Sep 6th 2017

Digital images of earth’s surface produced by remote sensing are the basis of modern mapping. They are also used to create valuable information products across a spectrum of industries. This free online course is for everyone who is interested in applications of earth imagery to increase productivity, save money, protect the environment, and even save lives.

Average: 3.8 (5 votes)

Sep 4th 2017

Learn how to use the free, open source process mining framework (ProM) to analyse, visualise, and improve processes based on data. Process mining combines business process management with data science. Using process mining, you can analyse and visualise business processes based on event data recorded in event logs. For example, you could analyse how people use public transportation; verify whether a loan application is processed correctly by a bank; or predict when hardware parts are likely to fail. This online course will give you an introduction to this new and exciting field.

Average: 7 (1 vote)
Aug 28th 2017

Science is undergoing a data explosion, and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations required to model our observable Universe push supercomputers to their limits. To analyse this data scientists need to be able to think computationally to solve problems.

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Aug 28th 2017

This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach.

Average: 6.3 (3 votes)

Aug 28th 2017

La toma de decisiones está en la esencia de los negocios. Gerenciar es tomar decisiones, muchas veces bajo presión, con información desordenada y en un contexto de incertidumbre. Un aspecto básico es entender y analizar la información, organizar los datos de forma de facilitar su posterior uso y la toma de decisiones.

Average: 7.3 (6 votes)
Aug 28th 2017

Learn to use the tools that are available from the Galaxy Project. This is the second course in the Genomic Big Data Science Specialization.

Average: 1 (4 votes)
Aug 28th 2017

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more.

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Aug 28th 2017

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 Specialisation. In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, Github, R, and Rstudio.

Average: 4.7 (26 votes)
Aug 28th 2017

The use of Excel is widespread in the industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel in their day to day functioning. This course is designed to give you a working knowledge of Excel with the aim of getting to use it for more advance topics in Business Statistics.

Average: 7.7 (10 votes)

Aug 28th 2017

This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed.

Average: 6.2 (5 votes)