Covers flow control concepts and file input/output operations, including loops, conditions, and handling command line arguments.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.
