Python Complement: Numpy, Scipy, Matplotlib

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Python Modules and Functions

Covers Python libraries, modules, scoping, functions, lambdas, and practical examples.

Functions in Python

Introduces functions in Python, covering predefined and user-defined functions, formal and effective parameters, and the importance of docstrings.

Python Basics: Functions and Lists

Introduces Python basics, functions, lists, and lambda functions for concise coding.

Data Science with Python: Modules and Numpy

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

Python Programming: Control Structures and Functions

Covers advanced topics in Python programming, focusing on control structures and functions.

Python Crash Course

Provides a Python crash course covering basic arithmetics, complex numbers, strings, lists, dictionaries, and more.

Python Functions: Basics and Arguments

Covers the basics of writing functions in Python and working with function arguments.

Introduction to C Programming

Covers the basics of C programming, including syntax, data types, pointers, arrays, and compilation commands.

Newton's Method: Convergence and Applications

Covers the convergence of Newton's method and its applications in numerical analysis.

General Introduction to Data Science

Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.