Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Covers the heterogeneous neuroscience data, techniques like microarrays and gene sequencing, data integration, and the importance of metadata in organizing and sharing data.
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
Discusses advanced Spark optimization techniques for managing big data efficiently, focusing on parallelization, shuffle operations, and memory management.
