Data Science

Quantum computing is challenging the realities of technology, security, and industry as we know them. Here, we investigate the nuances of quantum mechanics and how to enter the world of quantum software development with tools such as Cirq and TensorFlow Quantum.

Image labeling can be a tedious, time-consuming task, compounded by the sheer volume of data needed to train deep neural networks. This article breaks down large data set processing and explains how a new SaaS product can help automate image labeling.

Apache Spark is an analytics engine that can handle very large data sets. This guide reveals strategies to optimize its performance using PySpark.

Explore X (formerly Twitter) data clusters to uncover user behaviors (e.g., repost and reply patterns) within online communities. This guide focuses on a politically charged data set to illustrate the process of visualizing and analyzing social data.

Python and R empower data scientists to solve problems using elegant syntactic sugar, simplifying coding and solution exploration. Each language brings its unique capabilities and approach to bear.

Centrality and text analysis allow users to get more out of their social network data. Here’s how you can leverage them using R and Gephi.

Colloquial language doesn’t lend itself to computation. That’s where natural language processing steps in. Learn how topic modeling helps computers understand human speech.

Thanks to rapid advances in technology, large amounts of data generated on social networks can be analyzed with relative ease, especially for those who use the R programming language and Gephi.

Data inundates us like never before—how can we hope to analyze it? Graphs (networks, not bar graphs) provide an elegant approach. Find out how to start with the Python NetworkX library to describe, visualize, and analyze “graph theory” datasets.