Data Science with Python: Modules and Numpy

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Introduces the basics of Numpy, a numerical computing library in Python, covering advantages, memory layout, operations, and linear algebra functions.

Python Complement: Numpy, Scipy, Matplotlib

Covers advanced Python topics like numpy operations, scipy linear algebra, and matplotlib for creating figures.

Data Science for Engineers: Part 2

Explores data manipulation, exploration, and visualization in data science projects using Python.

Numpy Tutorial

Covers the basics of Numpy, including importing the library, creating arrays, and performing linear algebra operations.

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.

Python Recap: Modules and Objects

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

Data Science: Python for Engineers - Part II

Explores data wrangling, numerical data handling, and scientific visualization using Python for engineers.

Python/NumPy Primer

Introduces Python basics and NumPy for scientific computing, covering data types, functions, arrays, indexing, and common operations.

NumPy Arrays and Graphical Representations: Introduction

Covers NumPy arrays and their graphical representations using Matplotlib, focusing on array creation, manipulation, and visualization techniques.

NumPy: Array Manipulation and Broadcasting

Covers array creation, indexing, manipulation, and broadcasting using NumPy in Python.