Data Science 101: Python Data Visualization for Beginners

Wondering how to make the most of your data?

Transform your data into compelling stories and rich sources of information for your next pitch. This class is made for business professionals or aspiring data scientists that understand the fundamentals of coding (but not much more than that). All you need is a laptop, internet, and 30 minutes.

To focus the class on a concrete application, we study an A/B test comparing the effectiveness of two webpages. However, the process and the skills -- experimental design, data collection, and data analysis -- are broadly applicable.

Prerequisites. Don’t have coding or database experience? No worries.

If you’re unfamiliar with Python or SQL, don’t worry. There are two classes you can take to get up to speed. Each class takes about an hour.

Python: To get started, you can take my Coding 101: Python for Beginners course. In that class, you’ll start from ground zero, learning just the bits of programming that you’ll need. In an hour, you’ll have coding fundamentals needed for this course.

SQL: To get familiar with databases, you can take my SQL 101: Databases for Beginners course as well. In 45 minutes, you’ll have the database fundamentals needed for this course. We won’t explicitly use databases in this class. However, we’ll use related concepts (e.g., GROUP BY)

If you’re already familiar with Python and SQL, feel free to dive right in.

Want to learn more?

Interested in creative coding? Check out my VR 101 (AFrame Nature Scenes) class.

Interested in more data science or machine learning? Check out my Coding 101 (Python), SQL 101 (Database Design), or Computer Vision 101 (Applied ML) classes.

Follow me on Skillshare to be the first to hear about more courses in these areas!

Acknowledgements: B-roll used in introductory video CC-0 licensed by fauxels, vimeo, Olya Kobruseva on Pexels.

• Introduction
• Project: A/B Webpage Test
• Python Refresher
• Privacy-First Experimental Design
• Preprocessing Data in Pandas
• Analyzing Data in Pandas
• Visualizing Data in Matplotlib
• Conclusion