Data Science at Scale is Challenging.

Pandas is the most popular tool for data science, with millions of dedicated users. With over 600 functions, pandas enable data scientists to quickly and flexibly clean, transform, and summarize data. But pandas break down on large datasets, leading to out-of-memory errors and slow performance. At scale, the only alternative is to use the so-called “big data” frameworks, such as database systems or Spark. Learn how Ponder's open-source technology solves these challenges by empowering data science at scale!

Ponder’s open-source tools are based on decades of academic research and development, with multiple published papers at the top academic conferences and journals. The tools draw on multiple computer science disciplines, including human-computer interaction, database systems, distributed systems, machine learning, and data science, and visualization. For instance, Modin’s innovative design draws on scalability ideas from database systems and distributed systems—and applies it to data science tools like pandas.