Python is an open-source (free), general-purpose (unlimited), programming language which is the most popular for doing data science. Many companies and businesses are now using Python to gain insights from their data, which helps them to have competitive advantage. This course will take a practical approach by using codes written in Jupyter notebooks (a tool that most Data Scientists use on a daily basis) to teach you how to program with Python with the focus on doing data science.
Numerical Computation with NumPy
Numerical Computation with NumPy
The field of data science involves a lot of numerical computation. This module will adequately equip you with the skills that you need to understand and perform these computations effortlessly with Numerical Python (NumPy).
In this module, you will learn:
- About Python's popular library NumPy.
- How NumPy is used for numerical computations.
- What NumPy arrays are and why you need for them.
- How to create NumPy arrays from a Python list.
- How to create NumPy arrays using built-in methods.
- How to determine the shapes and axes of NumPy arrays.
- How to index and slice NumPy arrays.
- How to handle missing data with NumPy.
- How to use the attributes and methods of NumPy arrays.
- How to perform NumPy mathematical operations.
- How to generate different reliable random arrays with NumPy.
You will receive links to download this course in zip format during Checkout, along with an emailed link that will last for 30 days.