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Covers data stream processing concepts, focusing on Apache Kafka and Spark Streaming integration, event time management, and project implementation guidelines.
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.
Examines anonymous communications using Tor to protect privacy and navigate internet censorship, discussing its history, functionality, and limitations.
Explores precision oncology, focusing on predictive biomarkers, big data analysis, NGS applications, immune response mechanisms, and the personalization of immuno-oncology.
Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
