# Learning R Versus Python

2019-06-12

Yesterday I ran a simple Twitter poll about the relative ease of learning R vs. Python. Although a correct answer to this query will ALWAYS have to be based on nuances like pre-existing skills and the scope of need, this originates from people telling me they encounter job or career profiles that list a need for R and/or Python. If they don’t have either, if they prioritised the pursuit of just one, which would be possible to develop a degree of competency more easily, more quickly and more efficiently?

Andy has also created a Twitter moment from the responses.

My thought, based only on the question itself, is that R would be better than Python because the hypothetical person has no additional programming skills. For someone with additional programming skills, the breakdown for me starts with, if your background is statistics, database development, or functional programming, you probably want R; if your background is object-oriented development or imperative programming, you probably want Python. And then it gets nuanced.

## Lasso and Ridge Regression in Python

2019-06-26

Kristian Larsen shows off a few regression techniques using Python: Variables with a regression coefficient equal to zero after the shrinkage process are excluded from the model. Variables with non-zero regression coefficients variables are most strongly associated with the response variable. Therefore, when you conduct a regression model it can be helpful to do a […]

## Using Cohen’s D for Experiments

2019-06-21

Nina Zumel takes us through Cohen’s D, a useful tool for determining effect sizes in experiments: Cohen’s d is a measure of effect size for the difference of two means that takes the variance of the population into account. It’s defined asd = | μ1 – μ2 | / σpooledwhere σpooled is the pooled standard deviation over both cohorts. […]

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June 2019
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