Covers data stream processing with Apache Kafka and Spark, including event time vs processing time, stream processing operations, and stream-stream joins.
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.
Covers data stream processing concepts, focusing on Apache Kafka and Spark Streaming integration, event time management, and project implementation guidelines.
Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.
