Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Discusses advanced Spark optimization techniques for managing big data efficiently, focusing on parallelization, shuffle operations, and memory management.
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
Covers the fundamentals of data stream processing, including real-time insights, industry applications, and practical exercises on Kafka and Spark Streaming.
Delves into the 'digital turn' in history, examining historical research using digitized newspapers and exploring text reuse, word embeddings, and data visualization.
