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

Visualizations

Photo by Jenna Lee on Unsplash

Visualizing data is one of the most essential tasks for a data scientist. A picture is worth a thousand words, and a visualization can help you uncover various interesting aspects of your data, such as spikes, anomalies, clusters, trends, and more, enabling you to understand the story your data is telling.

In these five lessons, you will work with data sourced from nature and create engaging and visually appealing visualizations using different 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 created with 🌸 by Jen Looper, Jasleen Sondhi, and Vidushi Gupta.

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

🍄 Data on 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. The mushrooms are drawn from The Audubon Society Field Guide to North American Mushrooms (1981). This dataset was donated to UCI ML 27 in 1987.

🦆 Data on Minnesota Birds is sourced from Kaggle, scraped from Wikipedia by Hannah Collins.

All these datasets are licensed under CC0: Creative Commons.

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Disclaimer:
This document has been translated using the AI translation service Co-op Translator. While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.