Statistics & Data Analysis

 

 


 

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Jun 19th 2017

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.

Average: 9 (1 vote)
Jun 12th 2017

Learn how to apply selected statistical and machine learning techniques and tools to analyse big data. Everyone has heard of big data. Many people have big data. But only some people know what to do with big data when they have it. So what’s the problem? Well, the big problem is that the data is big—the size, complexity and diversity of datasets increases every day. This means that we need new technological or methodological solutions for analysing data. There is a great demand for people with the skills and know-how to do big data analytics.

Average: 6.6 (5 votes)
Jun 6th 2017

Le MOOC « Fondamentaux pour le big data » permet d'acquérir efficacement le niveau prérequis en informatique et en statistiques pour suivre des formations dans le domaine du big data. Le big data offre de nouvelles opportunités d’emplois au sein des entreprises et des administrations. De nombreuses formations préparant à ces opportunités de métiers existent. Le suivi de ces formations nécessite des connaissances de base en statistiques et en informatique que ce MOOC vous propose d’acquérir dans les domaines de l’analyse, algèbre, probabilités, statistiques, programmation Python et bases de données.

Average: 5.5 (6 votes)
Jun 6th 2017

Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life. In the last decade, the amount of data available to organizations has reached unprecedented levels. Data is transforming business, social interactions, and the future of our society. In this course, you will learn how to use data and analytics to give an edge to your career and your life.

Average: 7.5 (2 votes)
Jun 5th 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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Jun 5th 2017

Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate for each. You will be able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools.

Average: 6 (6 votes)
Jun 5th 2017

Relational Database Support for Data Warehouses is the third course in the Data Warehousing for Business Intelligence specialization. In this course, you'll use analytical elements of SQL for answering business intelligence questions. You'll learn features of relational database management systems for managing summary data commonly used in business intelligence reporting.

Average: 4 (1 vote)
Jun 5th 2017

Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University.

Average: 8.3 (3 votes)
Jun 5th 2017

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.

Average: 8 (2 votes)
Jun 5th 2017

Learn critical concepts and practical methods to support research data planning, collection, storage and dissemination.

Average: 9.7 (3 votes)
Jun 5th 2017

Learn methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest. This statistics and data analysis course will introduce you to the essential notions of probability and statistics.

Average: 2 (2 votes)
Jun 5th 2017

The analytical process does not end with models than can predict with accuracy or prescribe the best solution to business problems. Developing these models and gaining insights from data do not necessarily lead to successful implementations. This depends on the ability to communicate results to those who make decisions.

Average: 8.8 (4 votes)
Jun 5th 2017

This course provides an analytical framework to help you evaluate key problems in a structured fashion and will equip you with tools to better manage the uncertainties that pervade and complicate business processes. The course aim to cover statistical ideas that apply to managers. We will consider two basic themes: first, is recognizing and describing variations present in everything around us, and then modeling and making decisions in the presence of these variations.

Average: 8.5 (2 votes)
Jun 5th 2017

This is the fourth course in the Data Warehouse for Business Intelligence specialization. Ideally, the courses should be taken in sequence. In this course, you will gain the knowledge and skills for using data warehouses for business intelligence purposes and for working as a business intelligence developer. You’ll have the opportunity to work with large data sets in a data warehouse environment and will learn the use of MicroStrategy's Online Analytical Processing (OLAP) and Visualization capabilities to create visualizations and dashboards.

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Jun 5th 2017

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance.

Average: 7.1 (11 votes)
Jun 5th 2017

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

Average: 6 (4 votes)
Jun 5th 2017

Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

Average: 9 (1 vote)
Jun 5th 2017

Data about our browsing and buying patterns are everywhere. From credit card transactions and online shopping carts, to customer loyalty programs and user-generated ratings/reviews, there is a staggering amount of data that can be used to describe our past buying behaviors, predict future ones, and prescribe new ways to influence future purchasing decisions. In this brand new course, four of Wharton’s top marketing professors will dive deeper into the key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices including Amazon, Google, and Starbucks to name a few.

Average: 6.2 (11 votes)
Jun 5th 2017

Este curso te proporcionará las bases del lenguaje de programación estadística R, la lengua franca de la estadística, el cual te permitirá escribir programas que lean, manipulen y analicen datos cuantitativos. Te explicaremos la instalación del lenguaje; también verás una introducción a los sistemas base de gráficos y al paquete para graficar ggplot2, para visualizar estos datos. Además también abordarás la utilización de uno de los IDEs más populares entre la comunidad de usuarios de R, llamado RStudio.

Average: 6.8 (6 votes)
Jun 5th 2017

This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary.

Average: 6.7 (3 votes)

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