Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.
Covers clustering time series data using dynamic time warping, string metrics, and Markov models.
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
Covers the analysis of ergometry figures using Excel and the interpretation of respiratory and cardiovascular parameters.
Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
