This learning path will guide you through the techniques and methods used to visualize data using Matplotlib and Seaborn Python libraries.
In the first course, we introduce the MatPlotLib and Seaborn libraries and the methods available. In the second course, we will explore the main functionalities of Matplotlib: we will look at how to customize Matplotlib objects, how to use various plotting techniques, and finally, we will focus on how to communicate results.
You will then put your skills into practice in a real-world scenario in which you use Python to build and explore a dataset of financial returns using data related to the closing price of three stocks quoted in the NASDAQ 100 index.
If you have any feedback related to this learning path, feel free to contact us at firstname.lastname@example.org.
- Learn the fundamentals of Python's Matplotlib and Seaborn libraries
- Understand the different plot types available
- Customize objects
- Create multiple plots
- Customize plots (annotations, labels, linestyles, colors, etc)
- Data scientists
- Anyone looking to create plots and visualize data using Python
To get the most out of this learning path, you should already be familiar with using Python, for which you can take our Introduction to Python learning path. Knowledge of Python's Pandas library would also be beneficial and you might want to take our Working with Pandas and Data Wrangling with Pandas courses before embarking on this learning path.
The data used in this learning path can be found in this GitHub repository.
Andrea is a Data Scientist at Cloud Academy. He is passionate about statistical modeling and machine learning algorithms, especially for solving business tasks.
He holds a PhD in Statistics, and he has published in several peer-reviewed academic journals. He is also the author of the book Applied Machine Learning with Python.