Pandas is an open-source software library for the Python programming language designed for data manipulation and analysis. It is primarily used for data cleaning, preparation, and analysis through specialized data structures, namely the DataFrame and Series, which are optimized for handling numerical tables and time-series data.

Pandas Library

https://www.coursera.org/learn/python-data-analysis

University of Michigan - Ann Arbor

These online courses can be easy resources to structure data science education. 

Helpful data science online courses

Udemy's Data Science 2020 course is a comprehensive introduction to machine and deep learning in Python. The course covers statistics, elementary coding in Python, and machine & deep learning. The course emphasizes the use of sklearn and statsmodels for machine learning and TensorFlow for deep learning. The course has 29 hours of lectures in addition to hands-on exercises for each concept. The course can be found at [www.udemy.com/course/the-data-science-course-complete-data-science-bootcamp](https://www.udemy.com/course/the-data-science-course-complete-data-science-bootcamp) and is typically available for \$10-\$14.

Udemy.com: "The Data Science Course 2020: Complete Data Science Bootcamp"

Udemy's R Programming for Statistics and Data Science is a 6.5 hour introductory course to R. It starts from the very basics of coding and provides lots of hands on exercises to build your skills as you explore new concepts. It emphasizes matrix manipulation and dataframes, essential for data science, and includes lots of lessons on data visualization with R's famous ggplot2. The course can be found here: [www.udemy.com/course/r-programming-for-statistics-and-data-science/](https://www.udemy.com/course/r-programming-for-statistics-and-data-science/) 

R Programming for Statistics and Data Science

The open sourced class includes lecture pages, hands on lectures, discussions, jupyter notebooks... Very useful for very deep theoretical understanding.
[github.com/eecs445-f16/umich-eecs445-f16](https://github.com/eecs445-f16/umich-eecs445-f16) 

EECS 445 Github Notes

https://www.coursera.org/learn/python-machine-learning?utm_source=gg&utm_medium=sem&utm_content=01-CatalogDSA-ML1-US&campaignid=9918777773&adgroupid=100491712477&device=c&keyword=&matchtype=b&network=g&devicemodel=&adpostion=&creativeid=432357975999&hide_mobile_promo&gclid=CjwKCAjwnef6BRAgEiwAgv8mQb1CwbQkIUWRMGczmdtUo6LaKB5U4kg_Tet6Vk7sTpzIvIZtBwSxyxoC26sQAvD_BwE

Coursera: Applied Machine Learning in Python

Coursera's five-course Deep Learning Specialization covers neural networks, hyperparameter tuning, regularization, optimization, machine-learning project strategy, convolutional neural networks, and sequence models. The courses are Neural Networks and Deep Learning; Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization; Structuring Machine Learning Projects; Convolutional Neural Networks; and Sequence Models. https://www.coursera.org/specializations/deep-learning

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