## Podcast 371: Exploring the magic of instant Python refactoring with Sourcery

Who needs a pair programmer?

Essays, opinions, and advice on the act of computer programming from Stack Overflow.

Who needs a pair programmer?

In the eighth and final lesson of the series we’ll practice everything we’ve learned to date by loading and preparing some data, fitting a few different models, and comparing them. This is an open-ended session where we’ll examine some questions from our audience and demonstrate what a data analysis workflow could look like. If you…

Here's a collection of resources on how to get started using Python.

In the seventh lesson of the series we’ll discuss some methods for comparing linear regression models. In the process, we’ll learn about the problem of overfitting and investigate some of the pros and cons of various evaluation methods (such as R-squared, adjusted R-squared, log likelihood, AIC, and BIC). We’ll also continue to practice our Python…

In the sixth lesson of the series we’ll discuss some methods for data transformation to improve a linear regression model. In the process, we’ll learn to simulate data with known properties, review some of the assumptions of linear regression, and continue to practice our Python skills. Here are some Stack Overflow questions related to the…

In the fifth lesson of the series we’ll learn how to build more flexible linear models by adding interaction and polynomial terms. We’ll fit and inspect our models both mathematically and visually to understand how they work. In the process, we’ll continue to practice our Python skills and discuss some of the merits (and drawbacks)…

In the fourth lesson of the series, we’ll talk about the matrix representation of the linear regression problem. In the process, we’ll discuss the basics of matrix multiplication. We’ll also see how this mathematical understanding can prepare us to make sense of error messages that we might encounter when fitting a model in Python. Here…

In the third lesson of the series, we’ll implement our first linear regression model with multiple predictors (this is called “multiple linear regression”). As an example, we’ll use a simulated dataset to predict student quiz scores. In the process, we’ll again practice our graphing and Python skills. Here are some Stack Overflow questions related to…

In the second lesson of the series, we’ll learn how to fit and interpret a simple linear regression with a categorical predictor. We’ll use a simulated dataset to predict the amount of time someone will spend on a website based on the browser they are using. We’ll also predict the rental prices of NYC apartments…

Linear regression is a machine learning technique for modeling continuous outcomes. It is used for both prediction and data analysis in a variety of different fields. It is also the basis for a number of other machine learning models, including logistic regression and poisson regression. For anyone who is interested in learning more about data…

Creating a web app shouldn't require mastering three languages and four frameworks.

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