Visualization is meant to compare and contrast data, which lets you see patterns, glean insights, and all that. However, if we focus specifically on finding or displaying differences, some methods are more helpful than others. This guide describes five ways to get this focus.
Have you ever been embarrassed by the first iteration of one of your machine learning projects, where you didn’t include obvious and important features? In the practical hustle and bustle of trying to build models, we can often forget about the observation step in the scientific method and jump straight to hypothesis testing.
A/B testing is essential to how zulily operates a data-driven business. They use it to assess the impact of new features and programs before rolling them out. This blog post focuses on some of the more practical aspects of A/B testing.
For many data scientists, data manipulation begins and ends with Pandas or the Tidyverse. In theory, there is nothing wrong with this notion. Yet, these options can often be overkill for simple tasks like delimiter conversion. Aspiring to master the command line should be on every developer’s list, especially data scientists.
Among data scientists and analysts, R has long held its own against Python in the battle to determine the “best” analytical programming language. Statisticians and numerically-savvy academics of all stripes have typically preferred R to Python, so lots of R&D gets done in R. If you want to stay in the know, here are 7 people you should follow on Twitter.