Statistics & Data Analysis

 

 


 

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May 9th 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.

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May 1st 2017

Master the essentials of machine learning and algorithms to help improve learning from data without human intervention. Machine Learning is the basis for the most exciting careers in data analysis today. You’ll learn the models and methods and apply them to real world situations ranging from identifying trending news topics, to building recommendation engines, ranking sports teams and plotting the path of movie zombies.

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Apr 24th 2017

Welcome to this course on Data Analytics for Lean Six Sigma. In this course you will learn data analytics techniques that are typically useful within Lean Six Sigma improvement projects. At the end of this course you are able to analyse and interpret data gathered within such a project. You will be able to use Minitab to analyse the data. I will also briefly explain what Lean Six Sigma is.

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Apr 24th 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)
Apr 24th 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)
Apr 24th 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)
Apr 24th 2017

Learn how to model social and economic networks and their impact on human behavior. How do networks form, why do they exhibit certain patterns, and how does their structure impact diffusion, learning, and other behaviors? We will bring together models and techniques from economics, sociology, math, physics, statistics and computer science to answer these questions.

Average: 7.5 (2 votes)
Apr 24th 2017

In this course you will learn how to evaluate recommender systems. You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity, product coverage, and serendipity. You will learn how different metrics relate to different user goals and business goals.

Average: 10 (1 vote)
Apr 24th 2017

This course will expose you to the data analytics practices executed in the business. We will explore such key areas of data analytics as the analytical process, how data is created, stored, and accessed, and how the organization works with data and creates the environment in which analytics can flourish.

Average: 5 (1 vote)
Apr 24th 2017

Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics.

Average: 8.7 (6 votes)
Apr 24th 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)
Apr 24th 2017

This course will expose you to the data analytics practices executed in the business. We will explore such key areas of data analytics as the analytical process, how data is created, stored, and accessed, and how the organization works with data and creates the environment in which analytics can flourish.

Average: 1.7 (3 votes)
Apr 24th 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)
Apr 24th 2017

Organizations large and small are inundated with data about consumer choices. Knowing how to interpret data is the challenge -- and marketers in particular are increasingly expected to use analytics to inform and justify their decisions. This course gives you the tools to measure brand and customer assets, perform regression analysis, and design experiments as a way to evaluate and optimize marketing campaigns.

Average: 6 (4 votes)
Apr 24th 2017

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective.

Average: 8 (1 vote)
Apr 24th 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)
Apr 24th 2017

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather than a computer system or sensors, and are thus especially valuable for discovering knowledge about people’s opinions and preferences, in addition to many other kinds of knowledge that we encode in text.

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Apr 24th 2017

Want to learn the basics of large-scale data processing? Need to make predictive models but don’t know the right tools? This course will introduce you to open source tools you can use for parallel, distributed and scalable machine learning.

Average: 8.4 (9 votes)
Apr 24th 2017

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery.

Average: 7.8 (5 votes)
Apr 24th 2017

If you’ve ever skipped over`the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place. You may be a clinical practitioner reading research articles to keep up-to-date with developments in your field or a medical student wondering how to approach your own research. Greater confidence in understanding statistical analysis and the results can benefit both working professionals and those undertaking research themselves.

Average: 3.5 (4 votes)

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