Transition Python to Matlab/Octave

Related lectures (31)

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Covers the creation of ndArray objects with Numpy, including intervals, random values, manipulation, operations, and copying.

Covers Python modules, objects, and data containers like lists and NumPy arrays.

Covers debugging techniques in Python and computational methods like frequency power calculation and bird sound detection.

Covers elementary numerical methods and algorithmic thinking using Python for scientific computing.

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

Covers Python list manipulation and comprehension, emphasizing memory representation and mutability.

Covers the transition to Python and Jupyter notebooks for Numerical Analysis at EPFL, including interactive exercises and graded tests.

Covers the Python/Git test results and introduces the first Matlab/Octave exercises.

Covers wind power production analysis using Python and matplotlib.

Explores data wrangling with Hadoop, emphasizing storage formats and Hive for big data processing.