Oil Spills Analysis

Related lectures (30)

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Delves into advanced Spark optimization techniques, emphasizing data partitioning, shuffle operations, and memory management.

Introduces Python programming basics, covering data types, operators, variables, functions, and code tracing.

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

Covers decision tree classification using KNIME Analytics Platform for data preprocessing and model creation.

Covers the training of a binary sentiment classifier using an RNN.

Covers the basics of object-oriented programming in Python, including objects, classes, inheritance, and input/output handling.

Covers the basics of programming, including types, variables, methods, functions, conditions, loops, and boolean logic.

Introduces collaborative data science tools like Git and Docker, emphasizing teamwork and practical exercises for effective learning.

Introduces Python modules and NumPy for efficient array operations and linear algebra in data science.

Covers the derivation of external energy in geometric computing and practical Python implementations.