Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Covers the basics of polymer biophysics, including DNA conformation modeling and the Boltzmann principle, emphasizing the exponential decay of correlations between segments.
Explores the impact of big data, covering economic value, latency-sensitive and throughput-bound applications, graph analytics, and challenges in utilizing flash storage.
Explores autonomously moving and assembling soft matter systems, focusing on hydrogel actuators, latch-mediated spring actuation, and self-spinning filament bundles.
