Data Science

Logistic regression in R models a binary outcome, like pass or fail. It answers yes-or-no questions that linear regression handles badly. The model fits with glm(), a function built into base R. No package install happens, and no extra setup blocks your start. Students reach logistic regression the moment a linear model breaks. The outcome […]

Reading a CSV file in R takes one line of code: read.csv(“yourfile.csv”). The function loads your data into a data frame, which is the table-like structure R uses for almost every analysis. The harder part is what happens before and after that single line: putting the file in a place R looks at by default,

The “could not find function” error is one of the most common errors R students hit in their first weeks of using the language. The error message looks intimidating, but the cause is almost always simple. R is telling you that the function you typed is not currently loaded, not currently spelled correctly, or not

Every linear regression in R rests on four assumptions about the data. When the assumptions hold, the slope estimates, the p-values, and the confidence intervals are all trustworthy. When the assumptions break, those numbers become unreliable, even if the regression itself runs without an error message. R produces four diagnostic plots that test each of

Linear regression in R needs one function: lm(). You hand it a formula and a dataset, and it draws a straight line through your data. That is most of the job done. The rest is reading the output and checking a few plots to make sure the line is a fair summary of the data.

What languages and tools do you need to know and learn how to use to perform data mining, machine learning, big data or stream processing tasks? We have talked long about various programming tools, and languages focused on the analysis, extraction and manipulation of large volumes of data (data mining or data crunching). The fever for Big