Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Explores the societal impact of science, advocating against limiting it within boundaries, and introduces 'experimental history' as a multidisciplinary approach to studying science.
Delves into the 'digital turn' in history, examining historical research using digitized newspapers and exploring text reuse, word embeddings, and data visualization.
Explores data handling fundamentals, including models, sources, and wrangling, emphasizing the importance of understanding and addressing data problems.
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
