This lecture covers data manipulation and exploration using Python with a focus on visualization techniques. Topics include reshaping dataframes, handling missing values, and combining sensor measurements. Practical exercises involve resampling, interpolating, and visualizing data to identify correlations and trends.
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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.
Focuses on advanced pandas functions for data manipulation, exploration, and visualization with Python, emphasizing the importance of understanding and preparing data.
