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Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
Covers the calculation of stiffness matrices for each bar element in the global reference frame and explores the influence of element numbering and node positions.
Introduces a 'professional' 3D measurement system for stone analysis and feature extraction using stereo photogrammetry and structured light technologies.
