Learn numerical python to gain practical knowledge in how the NumPy package is used in scientific computing.

NumPy is used by Data Scientists, used in the fields of machine learning, used in data visualization, used in data evaluation, and the likes with its high-level syntax.

In this course, we would learn lots of different methods used in scientific computing, exploring the Numpy package with lots of exercises including handling or fixing some of the errors we might encounter, slicing, reshaping, converting a list to a NumPy array for fast processing.

The course assumes you already have python3, Anaconda already installed and you're comfortable using Jupyter notebook. Also, some background understanding of python basics is okay.

You'll have free -downloadable access to the course activities/ exercise from the first section of the course module. The jupyter notebook exercise file has been well commented on so you understand what we are trying to achieve with each line of code.

This should help you practice on your own while watching the video.

Also, more sessions will be added as they are being edited.

*Python 3* is the version of python used in the lectures and Jupyter notebook is the IDE used in programming for the course.

It should be noted that python and anaconda installations and downloads and setting up anaconda and python is not taught in this course.