DataScience

Explore Twitter data clusters to discover keywords that dominate identified groups. Focusing on a politically slanted data set provides an easy target for analysis.

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.

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.

IMDb ratings have genre bias: For example, dramas tend to score higher. Removing common feature bias and keeping unique characteristics, it's possible to create a new, refined score based on IMDb information.

With the rise of big data and data science, storage and retrieval have become a critical pipeline component for data use and analysis. Recently, new data storage technologies have emerged. But the question is: Which one should you choose? Which one is best suited for data engineering? In this article, Toptal Data Scientist Ken Hu compares three prominent storage technologies within the context of data engineering.

Analysts have come to recognize social network data as a virtual treasure trove of information for sensing public opinion trends and groundswells of support. In this article, Toptal Engineer Elder Santos describes the techniques he employed for a proof-of-concept that effectively analyzed Twitter Trend Topics to predict, as a sample test case, regional voting patterns in the 2014 Brazilian presidential election.