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A Short Primer On Generalized Linear Models (GLM)

Generalized Linear Models (GLM) refers to a large class of models which include the familiar ordinary linear regression — ordinary least squares (OLS) regression — and the analysis of variance (ANOVA) models. A bag loaded with tricks (models, rather) Both OLS regression and ANOVA deal with continuous response variables. However, there are times that we need to predict a categorical response variable, for example, yes/no responses and count data. For this purpose, other models like logit, log-linear, and probit model, just to name some, will be appropriate. Yes, there’s a lot of models in the GLM domain. It is easy…
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PyCon 2019 | Getting Started Testing in Data Science

PyCon 2019 | Getting Started Testing in Data Science Speaker: Jes Ford   How do you know if your data science results are correct? Robust software usually has tests asserting that certain conditions hold, but as a data scientist it’s often not straightforward or obvious how to integrate these best practices. Our workflow includes exploration, statistical models, and one-off analysis. This talk will give concrete examples of when and how testing should play a role, and provide you with enough introduction to get started writing your first data science tests using `pytest` & `hypothesis`.   Slides can be found at:…
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