Introduction to Python
for Data Science

This module will focus on building an understanding of how to Gather data from different sources. We will explore commonly used Python data structures such as data-frames and dictionaries and will learn about working with data from different data formats

In this module, we’ll be looking at Python’s powerful data handling techniques. You’ll be introduced to different methods that you will use to get the information you need from data stored in complex data structures.

Once we’ve loaded our data into your Python environment, we’ll learn how to process it before it can be used for further analysis. This includes cleaning the data, reshaping it, merging data from multiple sources/data structures, and applying transformations.

In this module, we learn about python’s powerful data aggregation and grouping techniques and how to use them to get useful insights from our data.

In this module, we will focus on understanding how to dissect and explore our data to uncover useful insights. We will also look at different visualization techniques to help present our data and findings and get acquainted with popular visualization packages in Python.

This module will be focused on the RESTful API, its functions and how it can be used to communicate with websites like Twitter and Reddit that have exposed endpoints to scrape data.

Once we’re comfortable with the RESTful API, we’ll look at how it can be used to get data from websites. We’ll use the data we scraped along with other data loading, parsing, cleaning and exploration techniques we learnt previously to build an end-to-end data pipeline.

Coming soon.