Fundamental Data Science

Many UCLA scholars doing basic research in data science, data theory, math, computer science, and statistics are often concerned with how to compute, measure, and code massive amounts of data. Fundamental data science explores questions about the nature of and patterns found in data, creating models for analysis, and making predictions from data.

You've probably heard the terms big data, machine learning and artificial intelligence. These ideas and more are at the center of fundamental and basic data science research at UCLA. Overall, the UCLA pillar of Fundamental Data Science includes concerns about the creation of knowledge, models and systems as well as the skills and tools to understand, synthesize and analyze large amounts of data. 

In the past fifty or so years, the field of fundamental data science has been concerned with how to make sense of large data sets. However, the study and use of data has fundamental roots in disciplines such as mathematics, statistics, information studies, and computer engineering. Today, the power of computer software to collect, organize, compute and analyze data is at the forefront of fundamental data science research and teaching.

Typically, fundamental data scientists are often concerned with problem definition, information sources and structures, architecture and methods for organizing and analyzing, making predictions from patterns in the data, and communicating in various ways the insights gained.