Statistical Inference

 

 


 

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E.g., 2017-08-18
E.g., 2017-08-18
E.g., 2017-08-18
Aug 21st 2017

Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses.

Average: 7 (10 votes)
Aug 21st 2017

This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power.

Average: 9 (5 votes)
Aug 21st 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: 7.2 (12 votes)

Aug 14th 2017

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.

Average: 7.9 (11 votes)
Jul 31st 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)
Jul 31st 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)

Jul 31st 2017

Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.

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Self-Paced

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences. We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R. We provide R programming examples in a way that will help make the connection between concepts and implementation.

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Self-Paced

A focus on the techniques commonly used to perform statistical inference on high throughput data. In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation.

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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: 7.3 (7 votes)
Jan 17th 2017

An introduction to probabilistic models, including random processes and the basic elements of statistical inference. The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.

Average: 9 (2 votes)

Jun 6th 2016

This course will introduce students to the major concepts and tools for collecting, analyzing and drawing conclusions from data.

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