Lectures related to Advanced Spark Optimization

Explores advanced Spark topics like partitioning strategies, memory optimization, and shuffle operations for efficient job execution.

Covers exception handling in Python, including try, raise, except, else, and finally blocks, with examples.

Covers scopes, lambdas, and pandas in data science with Python, including nested declarations, scoping, assignments, and pandas manipulation.

Covers the basics of Spark DataFrames, their advantages, performance comparison with RDDs, and practical demos.

Covers the basics of Python programming and setting up Anaconda and Jupyter Notebook.

Explores advanced GPU memory management, CUDA programming, and data-parallel computing for optimizing performance.

Covers Spark Data Frames, distributed collections of data organized into named columns, and the benefits of using them over RDDs.

Explores tools like Git and Docker for collaborative data science projects.