Python for MATLAB Users

Python is a more high-level language than Matlab, and many people consider it more user-friendly and readable. It also has considerably more libraries and frameworks (such as Tensorflow, Keras, PyTorch, etc.) which means that it is more widely used and could be considered more useful depending on your needs and applications. However, Matlab does have its strengths in mathematics and engineering.

There are a number of reasons why you may want to use Python over Matlab (and, as a result, invest in growing your Python skills). Python code is typically more compact and readable than Matlab and offers excellent support for dictionaries. Python also offers more choices in graphics packages, toolsets, and libraries and is free and open-source. Python’s control over code and namespace management makes it far easier to maintain multiple versions of shared libraries, and its data structures are typically better than those available in Matlab. What's more, it's also a dynamic language, giving a more adaptable coding style.Python is also more typical in that it uses zero-based indexing, unlike Matlab.

Yes, this course is a great introduction for MATLAB users looking to learn the fundamentals of Python. It provides a gentle, yet comprehensive introduction to the basic building blocks of Python such as syntax, numpy arrays, and matplotlib.

Yes, upon completion of the course you will receive a certificate of completion.

By taking this course you will acquire the skills needed to migrate from MATLAB to Python and quickly become proficient in the language, as well as learn to create custom data visualizations and insights from your data.

It depends on your learning pace and experience level, but typically the course takes about 4 hours to complete.

This course is designed for individuals who are looking to transition from MATLAB to Python for data analysis and visualization. It is ideal for software engineers, data analysts, data scientists, and other related roles.

This course covers basic syntax of Python, ways to use numpy arrays, creating data visualizations using matplotlib, discovering trends, correlations and patterns in data, understanding different data structures such as lists, dictionaries, and pandas DataFrames, and controlling algorithm flow with loops and contingencies.