31Oct 2018 by Andrew Treadway
How to run R from the Task Scheduler

How to run R from the Task Scheduler

R, System Administration
In a prior post, we covered how to run Python from the Task Scheduler on Windows. This article is similar, but it'll show how to run R from the Task Scheduler, instead. Similar to before, let's first cover how to R from the command line, as knowing this is useful for running it from the Task Scheduler. Running R from the Command Line To open up the command prompt, just press the windows key and search for cmd. When R is installed, it comes with a utility called Rscript. This allows you to run R commands from the command line. If Rscript is in your PATH, then typing Rscript into the command line, and pressing enter, will not result in an error. Otherwise, you might get a message saying "'Rscript'…
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31Oct 2018 by Andrew Treadway

Data Analysis with Python Course: How to read, wrangle, and analyze data

Pandas, Python
For anyone in the NYC area, I will be holding an in-person training session December 3rd on doing data analysis with Python. We will be covering the pandas, pyodbc, and matplotlib packages. Please register at Eventbrite here: https://www.eventbrite.com/e/data-analysis-with-python-how-to-read-wrangle-and-analyze-data-tickets-51945542516. Overview Learn how to apply Python to read, wrangle, visualize, and analyze data!  This course provides a hands-on session where we'll walk through a prepared curriculum on doing data analysis with Python.  All code and practice exercises during the session will be made available after the course is complete.     About the course During this hands-on class, you will learn the fundamentals of doing data analysis in Python, the powerful pandas package, and pyodbc for connecting to databases. We will walk through using Python to analyze and answer key questions on sales…
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02Oct 2018 by Andrew Treadway
How to build a logistic regression model from scratch in R

How to build a logistic regression model from scratch in R

Machine Learning, R
Background In a previous post, we showed how using vectorization in R can vastly speed up fuzzy matching. Here, we will show you how to use vectorization to efficiently build a logistic regression model from scratch in R. Now we could just use the caret or stats packages to create a model, but building algorithms from scratch is a great way to develop a better understanding of how they work under the hood. Definitions & Assumptions In developing our code for the logistic regression algorithm, we will consider the following definitions and assumptions: x = A dxn matrix of d predictor variables, where each column xi represents the vector of predictors corresponding to one data point (with n such columns i.e. n data points) d = The number of predictor…
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