Data Analysis

 

 


 

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E.g., 2017-02-28
E.g., 2017-02-28
E.g., 2017-02-28
Mar 21st 2017

En un mundo globalizado y cada vez más dinámico, la toma de decisiones correctas de forma ágil y eficiente es una actividad esencial en muchos ámbitos de nuestra actividad diaria. Para cualquier sector empresarial es fundamental contar con profesionales que sean capaces de combinar grandes cantidades de datos e información para llevar a cabo procesos de toma de decisiones a partir de evidencias objetivas. El curso va dirigido a todas aquellas personas que deseen obtener una visión introductoria y práctica sobre análisis de datos y big data. En particular, el MOOC se centra en conceptos, métodos y herramientas básicas para el procesado, análisis y construcción de modelos estadísticos con datos de muy diversa índole.

Average: 7 (1 vote)
Mar 9th 2017

Pas besoin d’être matheux pour être confronté, au quotidien, à une profusion de chiffres et de données statistiques. Dans les médias, sur les réseaux sociaux ou même dans les conversations ordinaires, il est courant d’argumenter, chiffres à l’appui. Pourtant, sommes-nous sûrs de savoir d’où viennent et comment ont été produites ces fameuses « données chiffrées » qui semblent faire autorité, ou qui animent tout au moins le débat ? Ce cours en ligne propose d’abord de revenir sur les raisons qui ont poussé les premiers chercheurs en sciences humaines et sociales à introduire les données statistiques dans leurs raisonnements, stabilisant des méthodes d’investigation de l’opinion et des pratiques qui se sont par la suite vulgarisées dans l’espace social.

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Mar 6th 2017

In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work.

Average: 8 (5 votes)
Mar 6th 2017

The value of IoT can be found within the analysis of data gathered from the system under observation, where insights gained can have direct impact on business and operational transformation. Through analysis data correlation, patterns, trends, and other insight are discovered. Insight leads to better communication between stakeholders, or actionable insights, which can be used to raise alerts or send commands, back to IoT devices. With a focus on the topic of Exploratory Data Analysis, the course provides an in-depth look at mathematical foundations of basic statistical measures, and how they can be used in conjunction with advanced charting libraries to make use of the world’s best pattern recognition system – the human brain.

Average: 5 (1 vote)
Feb 27th 2017

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

Average: 5.6 (24 votes)
Feb 27th 2017

Welcome to the Cloud Computing Applications course, the second part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data!

Average: 7.4 (5 votes)
Feb 27th 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: 5.2 (5 votes)
Feb 27th 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)
Feb 27th 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: 7.5 (2 votes)
Feb 27th 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)
Feb 27th 2017

This course will provide you with an overview over existing data products and a good understanding of the data collection landscape. With the help of various examples you will learn how to identify which data sources likely matches your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan.

Average: 10 (1 vote)
Feb 27th 2017

This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

Average: 7.2 (5 votes)
Feb 27th 2017

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.

Average: 8 (5 votes)
Feb 27th 2017

This course explores Excel as a tool for solving business problems. In this course you will learn the basic functions of excel through guided demonstration. Each week you will build on your excel skills and be provided an opportunity to practice what you’ve learned. Finally, you will have a chance to put your knowledge to work in a final project.

Average: 3.9 (8 votes)
Feb 27th 2017

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies.

Average: 6.2 (10 votes)
Feb 27th 2017

Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph analytics and want to learn more? This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyze graph-structured data. After completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner. Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects.

Average: 6 (1 vote)
Feb 27th 2017

Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on.

Average: 9 (2 votes)
Feb 27th 2017

This course builds on the theory and foundations of marketing analytics and focuses on practical application by demystifying the use of data in marketing and helping you realize the power of visualizing data with artful use of numbers found in the digital space.

Average: 10 (1 vote)
Feb 27th 2017

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.

Average: 8.3 (6 votes)
Feb 27th 2017

This course is an introduction into formal concept analysis (FCA), a mathematical theory oriented at applications in knowledge representation, knowledge acquisition, data analysis and visualization. It provides tools for understanding the data by representing it as a hierarchy of concepts or, more exactly, a concept lattice. FCA can help in processing a wide class of data types providing a framework in which various data analysis and knowledge acquisition techniques can be formulated. In this course, we focus on some of these techniques, as well as cover the theoretical foundations and algorithmic issues of FCA.

Average: 6 (2 votes)

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