Data Science Articles

Aria Voron

Updated on March 17, 2025

Too many companies create a huge stack of reports that nobody ever uses. Here’s how to change that.

Stephen Gossett

Updated on March 14, 2025

The ability of computers to quickly process and analyze human language is transforming everything from translation services to human health.

Anmol Tomar

Updated on March 14, 2025

The elbow method is a technique used to find the optimal number of clusters (K) in k-means clustering, by identifying the “elbow” point on a graph of k-values and their corresponding within-cluster sum of squares (WCSS) values.

Stephen Gossett

Updated on March 13, 2025

Gender data can be valuable for a variety of reasons. But before you ask, consider why you’re asking, and how you frame the question.

Zolzaya Luvsandorj

Updated on March 13, 2025

Propensity score matching is a causal inference technique that attempts to balance treatment groups on confounding factors, so researchers can gauge the treatment’s causal impact on the outcome. Here are the steps to conduct propensity score matching.

Zakaria Jaadi

Updated on March 13, 2025

Learn how to use a PCA when working with large data sets.

Claudiu Clement

Updated on March 13, 2025

Standard deviation measures the variability of data in relation to the mean, while standard error measures the variability of different sample means. Take a closer look at how to calculate each and the main differences between the two.

Esmaeil Alizadeh

Updated on March 13, 2025

Dynamic time warping (DTW) is a technique used to compare two temporal sequences that don’t perfectly sync. Here’s how it works, how to implement it and its key benefits.

Satyapriya Chaudhari

Updated on March 13, 2025

Descriptive statistics describe data through numbers — like mean, median and mode — and graphical methods — like histograms, box plots and pie charts — to make data easier to understand. Here’s an in-depth look at how to calculate them.

Valentina Alto

Updated on March 13, 2025

Ordinary Least Squares (OLS) regression is a technique used in linear regression to minimize the sum of squared differences between observed and predicted values, and obtain a straight line as close as possible to your data points.