Learn 12 techniques for how to work with data using Python. Review how to explore data, ingest data, visualize data, and more.
- [Ben] Python is one of the easiest and most powerful programming languages in use today. When it comes to working with data, Python has become an indispensable language used by organizations large and small. It's easy to learn, read, and can handle nearly any task you throw at it. Hi, I'm Ben Sullins, and in this course, we're going to take a look at tips for using Python in your data science workflow. We'll start by looking at how to ingest data into your workflow with Python, including working with different file formats and reading data from a web API.
In chapter two, we'll dig in deeper on how to work with the data using the powerful pandas library, a must for data science work in Python. Lastly, we'll see how we can easily visualize our analysis using ggplot, a powerful graphing library. We'll be covering all these topics to give you an edge in delivering high quality analysis to your users. Let's dive in.
- Working with flat files, including Parquet
- Reading data using APIs or libraries
- Inspecting and aggregating data with Pandas
- Exporting data with Pandas
- Creating charts using ggplot
- Styling plots using ggplot
- Finishing data visualizations