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At the end of this course, you will have a thorough understanding of Numpy' s features and when to use them. Numpy is mainly used in matrix computing. We'll do a number of examples specific to matrix computing, which will allow you to see the various scenarios in which Numpy is helpful. There are a few computational computing libraries available for Python. It's important to know when to choose one over the other. Through rigorous exercises, you'll experience where Numpy is powerful and develop and understanding of the scenarios in which Numpy is most useful. You'll also know how to install Numpy.

Scraping data from webpages can be a tedious job. But it doesn’t have to be. With Scrapy, you can scrape using XPath or CSS. With the large number of examples from both techniques, you’re sure to find a solution that fits for you. Whether your targeting data on a single page or multiple, Scrapy can handle the job. No matter if the data is within a list, you can scrape specific patterns right out of the list. Building up your specific Scrapy job isn't a difficult task. Scrapy is a Python library. If you're familiar with Python, XPath or CSS, you'll feel right at home using Scrapy.

Computational computing can be a complex topic. How to perform various mathematical functions in code isn't straight forward. With Python's Scipy library, we'll walk through a number of examples showing exactly how to create and execute complex computational computing functions.

A beginner’s guide to creating your own application with Python