Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, ...

This course offers an introduction to mathematical nonlinear optimization with applications in data science. The theoretical foundation and the fundamental algorithms for nonlinear optimization are ...

The intersection of evolutionary algorithms and data-driven optimisation is reshaping materials science by offering novel computational frameworks for designing and refining materials. Drawing ...

The massive datasets that power machine learning algorithms and systems are complex, noisy, and vulnerable to various kinds of errors, contamination, and adversarial corruptions. As data science and ...

Data Science is a structured approach to extracting valuable insights from data, and it involves several key stages to ensure success. Let's explore each phase in detail: By following this structured ...

Extracting and analyzing relevant medical information from large-scale databases such as biobanks poses considerable challenges. To exploit such "big data," attempts have focused on large sampling ...

The algorithms that underlie modern artificial-intelligence (AI) systems need lots of data on which to train. Much of that data comes from the open web which, unfortunately, makes the AIs susceptible ...

Develop interdisciplinary skills in data science and gain knowledge of statistical analysis, data mining, and machine learning from one of the nation’s top-ranked Tier 1 research institutions. Earn ...

Data science encompasses the theoretical and practical application of ideas, including Big Data, predictive analytics, and artificial intelligence. If data is the oil of the information age and ML is ...