Data Science Innovations: Insights, Challenges, and Future
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Covers the fundamentals of data stream processing, including tools like Apache Storm and Kafka, key concepts like event time and window operations, and the challenges of stream processing.
Delves into the energy-efficient distributed machine learning approach in the IoT era, emphasizing the importance of summarizing data for improved communication energy efficiency.
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.