Numpy is the most important external library we will use in this course, and it is important to become very comfortable with it. To start, please go through:
- Numpy Introduction from W3Schools. This interactive tutorial introduces you to the Numpy library and, helpfully, you can run the examples directly in the browser.
- Watch the following short LinkedIn Learning videos. These
- give you an overview of Numpy.
- explain how to create Numpy arrays
- teach you how to do mathematical operations with and on arrays
- show how to index and slice arrays into pieces
- Numpy's official website provides a valuable additional resource, Numpy: The absolute basics for beginners
Once you have a basic familiarity with Numpy, you can complete the LinkedIn Learning Numpy Data Science Essential Training and submit your certificate, as shown below, to complete this assignment. For information on how to turn in an assignment with Microsoft Teams, see the webpage.
- As you work through the course, please have a Jupyter notebook open and try the presented code snippets yourself.
- You may skip the "Numpy, data science, IMQAV" under the "Introduction" section.
- You may skip "Install Software" under the "Introduction" section.
- You may skip "7. Extended Examples"; while these are good Numpy practice cases, they are less relevant to us.
