Data-Science-For-Beginners/3-Data-Visualization

Visualizations

Photo by Jenna Lee on Unsplash

Visualizing data is one of the most important tasks of a data scientist. Images are worth 1000 words, and a visualization can help you identify all kinds of interesting parts of your data such as spikes, outliers, groupings, tendencies, and more, that can help you understand the story your data is trying to tell.

In these five lessons, you will explore data sourced from nature and create interesting and beautiful visualizations using various techniques.

Topic Number	Topic	Linked Lesson	Author
1.	Visualizing quantities	Python R	Jen Looper Vidushi Gupta Jasleen Sondhi
2.	Visualizing distribution	Python R	Jen Looper Vidushi Gupta Jasleen Sondhi
3.	Visualizing proportions	Python R	Jen Looper Vidushi Gupta Jasleen Sondhi
4.	Visualizing relationships	Python R	Jen Looper Vidushi Gupta Jasleen Sondhi
5.	Making Meaningful Visualizations	Python R	Jen Looper Vidushi Gupta Jasleen Sondhi

Credits

These visualization lessons were written with 🌸 by Jen Looper, Jasleen Sondhi and Vidushi Gupta.

🍯 Data for US Honey Production is sourced from Jessica Li's project on Kaggle. The data is derived from the United States Department of Agriculture.

🍄 Data for mushrooms is also sourced from Kaggle revised by Hatteras Dunton. This dataset includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family. Mushroom drawn from The Audubon Society Field Guide to North American Mushrooms (1981). This dataset was donated to UCI ML 27 in 1987.

🦆 Data for Minnesota Birds is from Kaggle scraped from Wikipedia by Hannah Collins.

All these datasets are licensed as CC0: Creative Commons.