fbpx

  • Warning: Invalid argument supplied for foreach() in /home/customer/www/opendatascience.com/public_html/wp-includes/nav-menu.php on line 95

    Warning: array_merge(): Expected parameter 2 to be an array, null given in /home/customer/www/opendatascience.com/public_html/wp-includes/nav-menu.php on line 102
Statistics
The Bayesians are Coming! The Bayesians are Coming, to Time Series

The Bayesians are Coming! The Bayesians are Coming, to Time Series

ModelingStatisticsbayesianWest 2020posted by ODSC Community September 28, 2020

Editor’s note: Aric is a speaker for ODSC West 2020 this October. Check out his talk, “The Bayesians are Coming! The Bayesians are Coming, to Time Series,” there!  Forecasting has applications across all industries. From needing to predict future values of sales for a product line,... Read more
Data Imputation: Beyond Mean, Median and Mode

Data Imputation: Beyond Mean, Median and Mode

ModelingStatisticsData Imputationposted by Ambar Kleinbort February 10, 2020

Types of Missing Data 1.Unit Non-Response Unit Non-Response refers to entire rows of missing data. An example of this might be people who choose not to fill out the census. Here, we don’t necessarily see Nans in our data, but we know there are values missing... Read more
From Idea to Insight: Using Bayesian Hierarchical Models to Predict Game Outcomes Part 2

From Idea to Insight: Using Bayesian Hierarchical Models to Predict Game Outcomes Part 2

ModelingStatisticsbayesianBayesian Hierarchical Modelsposted by Brandon Dey, ODSC January 30, 2020

What’s the best way to model the probability that one player beats another in a digital game a client of your employer designed? This is the second of a two-part series in which you’re a data scientist at a fictional mobile game development company that makes... Read more
From Idea to Insight: Using Bayesian Hierarchical Models to Predict Game Outcomes Part 1

From Idea to Insight: Using Bayesian Hierarchical Models to Predict Game Outcomes Part 1

ModelingStatisticsbayesianHierarchical Bayesian Modelsposted by Brandon Dey, ODSC January 17, 2020

Imagine you’re a data scientist at an online mobile multiplayer competition platform. Your bosses have a vested interest in paying people with our skillset to predict game outcomes for a variety of commercial applications they profit from, for example, setting odds and sharing better insights with... Read more
The Turf War Between Causality and Correlation In Data Science: Which One Is More Important?

The Turf War Between Causality and Correlation In Data Science: Which One Is More Important?

ModelingStatisticscausalityCorrelationposted by Leihua Ye January 6, 2020

Data scientists have tried to differentiate causality from correlation. Last month alone, I’ve seen 20+ posts referencing the catchphrase “correlation is not causality.” What they actually want to say is correlation is not as good as causality. [Related Article: Discovering 135 Nights of Sleep with Data,... Read more
Regression Discontinuity Design: The Crown Jewel of Causal Inference

Regression Discontinuity Design: The Crown Jewel of Causal Inference

ModelingStatisticscasual 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

A Quick Look Into Bootstrapping

Machine LearningModelingRStatisticsTools & LanguagesbootstrappingStatisticsposted 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
Discovering 135 Nights of Sleep with Data, Anomaly Detection, and Time Series

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

ModelingPythonRStatisticsTools & Languagesanomaly detectionTime 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 Common Regression Pitfalls in Business Applications

3 Common Regression Pitfalls in Business Applications

Business + ManagementModelingStatisticsregressionposted 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

Hierarchical Bayesian Models in R

ModelingRStatisticsTools & Languagesbayesianhierarchical bayesianHierarchical Bayesian ModelsStatisticsposted 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
Copyright © 2020 Open Data Science. All rights reserved.