Data Science Statistics Articles, Guide, and Tutorials

Regression Discontinuity Design: The Crown Jewel of Causal Inference

Modeling Statistics casual inferenceposted by Leihua Ye December 17, 2019

Background In a series of posts (here, here, here, here and here), I’ve explained why and how we should run social experimentations. However, it’s not possible to do social experiments all the time, and researchers have to identify causal effects by other observational and quasi-experimental methods. [Related Article: Causal Inference: An... Read more

A Quick Look Into Bootstrapping

Machine Learning Modeling R Statistics Tools & Languages bootstrapping Statisticsposted by Leihua Ye December 3, 2019

Executive Summary As a resampling method, bootstrapping allows us to generate statistical inferences about the population from a single sample. Learn to bootstrap in R. Bootstrapping lies the foundation for several machine learning methods (e.g., Bagging. I’ll explain Bagging in a follow-up post). [Related Article: Discovering... Read more

135 Nights of Sleep with Data, Anomaly Detection, and Time Series

Modeling Python R Statistics Tools & Languages anomaly detection Time Seriesposted by Juan De Dios Santos November 4, 2019

In this article, I look at data from 135 nights of sleep and use anomaly detection and time series data to understand the results. Three things are certain in life: death, taxes, and sleeping. Here, we’ll talk about the latest. Every night*, us humans, after a... Read more

3 Regression Pitfalls in Business Applications

Business + Management Modeling Statistics regressionposted by Jacey Heuer October 21, 2019

Regression is a fantastic tool for aiding business decisions. The traditional purpose of a regression model is to find the mean value of a dependent variable given a set of independent variables. In a business, this purpose should be expanded to include the reduction of uncertainty... Read more

Hierarchical Bayesian Models in R

Modeling R Statistics Tools & Languages bayesian hierarchical bayesian Hierarchical Bayesian Models Statisticsposted by Nathaniel Jermain August 19, 2019

Hierarchical approaches to statistical modeling are integral to a data scientist’s skill set because hierarchical data is incredibly common. In this article, we’ll go through the advantages of employing hierarchical Bayesian models and go through an exercise building one in R. If you’re unfamiliar with Bayesian... Read more

The Empirical Derivation of the Bayesian Formula

Guest contributor Machine Learning Modeling Statistics bayseian Machine Learningposted by Jannes Klaas June 18, 2019

Deep learning has been made practical through modern computing power, but it is not the only technique benefiting from this large increase in power. Bayesian inference is up and coming technique whose recent progress is powered by the increase in computing power. We can explain the... Read more

Why Do Tree Ensembles Work?

Guest contributor Machine Learning Modeling Statistics Machine Learning Statisticsposted by Joe Ross April 10, 2019

Ensembles of decision trees (e.g., the random forest and AdaBoost algorithms) are powerful and well-known methods of classification and regression. We will survey work aimed at understanding the statistical properties of decision tree ensembles, with the goal of explaining why they work. An elementary probabilistic motivation... Read more

The Importance of P-Values in Data Science

Modeling Statisticsposted by Daniel Gutierrez, ODSC February 26, 2019

The field of data science makes use of concepts from a variety of disciplines, particularly computer science, mathematics, and applied statistics. One term that keeps popping up in data science circles (including many interviews for data scientist employment positions) is “p-value” which comes from statistics. This... Read more

Confidence Intervals for Data Scientists

Modeling Statistics Statisticsposted by Daniel Gutierrez, ODSC January 17, 2019

Confidence interval is a basic statistical concept commonly employed by data scientists. Without a formal background in statistics, however, some data scientists tend to scratch their heads with respect to their understanding of what’s really going on with this notion. In this article, we’ll review the... Read more

How to Play Fantasy Sports Strategically (and Win)

Statistics Conferences Dirichlet Distribution Fantasy Football Martin Haugh ODSC Europe Statisticsposted by Paxtyn Merten November 30, 2018

Daily Fantasy Sports is a multibillion-dollar industry with millions of annual users. The Imperial College Business School’s Martin Haugh created a framework to best those users by modeling what they’ll do and constructing a team based on it. Haugh presented his research on how to play... Read more