Editing Advanced Computational Communication Methods (Summer 2023) (section)

===Resources added by Ryan===
'''Refresher/Basic resources'''
* Quick video overview: https://www.youtube.com/watch?v=a9UrKTVEeZA
* Longer, but still simple, video course outlining visualization techniques: https://www.simplilearn.com/tutorials/python-tutorial/data-visualization-in-python
* And of course, don't forget that one of the greatest resources for getting input on how to change visualizations is ChatGPT: https://chat.openai.com/

'''Understanding which visualization libraries to learn/use'''
* A useful academic article suggesting Matplotlib, Seaborn, and Plotly as the best: -	https://ieeexplore.ieee.org/abstract/document/8757088?casa_token=REAm2SOC93MAAAAA:fCJHaTYgHA8FXZMbVEdZcevcXKsNJBBvB83F5HGgSEh504YPfROjnI08K1f2CJ1b6ZDVhhxF 
* An excellent article on Medium about what use case scenarios are best for each of Matplotlib, Seaborn, and Plotly: https://medium.com/mlearning-ai/comparing-python-libraries-for-visualization-b2eb6c862542#:~:text=Matplotlib%20is%20a%20great%20choice,choice%20for%20creating%20interactive%20visualizations.

'''Matplotlib'''
* Excellent general overview: https://towardsdatascience.com/introduction-to-data-visualization-in-python-89a54c97fbed
* Great, more in-depth guide on how to really take visualizations to the next level: https://towardsdatascience.com/5-steps-to-build-beautiful-bar-charts-with-python-3691d434117a 
* Documentation: https://matplotlib.org/stable/index.html

'''Seaborn'''
* Great overview of Seaborn: https://medium.com/insight-data/data-visualization-in-python-advanced-functionality-in-seaborn-20d217f1a9a6
* Third-party documentation-style site that helps make it really easy to figure out how to do each kind of visualization: https://www.geeksforgeeks.org/python-seaborn-tutorial/ 
* Documentation: https://seaborn.pydata.org/

'''Plotly'''
* Excellent quick overview of what Plotly can do and how to use it: https://towardsdatascience.com/the-next-level-of-data-visualization-in-python-dd6e99039d5e
* Third-party documentation-style site that helps make it really easy to figure out how to do each kind of visualization: https://www.geeksforgeeks.org/python-plotly-tutorial/
* Documentation: https://plotly.com/python/

'''Visualization for Exploratory Data Analysis'''
* Academic article that goes over objectives and processes for EDA using visualizations: https://www.researchgate.net/profile/Dr-Subhendu-Pani/publication/337146539_IJITEE/links/5dc70b124585151435fb427e/IJITEE.pdf 
* Great article that shows how visualizations are really useful for EDA in even more NLP scenarios. For example, what are the distributions of sentiments?: -	https://medium.com/towards-data-science/a-complete-exploratory-data-analysis-and-visualization-for-text-data-29fb1b96fb6a
* EDA applied to Machine Learning contexts: https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-1-exploratory-data-analysis-with-pandas-de57880f1a68 and visualizations applied to machine learning contexts: https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-2-visual-data-analysis-in-python-846b989675cd
* A gentle introduction to EDA: https://towardsdatascience.com/a-gentle-introduction-to-exploratory-data-analysis-f11d843b8184