https://github.com/mxagar/statistics_with_python_coursera
My personal notes done while following the Coursera Specialization "Statistics with Python", from the University of Michingan, hosted by Dr. Brenda Gunderson.
https://github.com/mxagar/statistics_with_python_coursera
data-modeling data-science data-visualization hypothesis-testing machine-learning pandas python statistics
Last synced: about 1 year ago
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My personal notes done while following the Coursera Specialization "Statistics with Python", from the University of Michingan, hosted by Dr. Brenda Gunderson.
- Host: GitHub
- URL: https://github.com/mxagar/statistics_with_python_coursera
- Owner: mxagar
- Created: 2022-01-11T13:22:31.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-02-28T16:37:29.000Z (about 3 years ago)
- Last Synced: 2025-02-15T12:49:48.352Z (about 1 year ago)
- Topics: data-modeling, data-science, data-visualization, hypothesis-testing, machine-learning, pandas, python, statistics
- Language: Jupyter Notebook
- Homepage:
- Size: 65.7 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Statistics with Python - Coursera / University of Michigan
These are my personal notes taken while following the Coursera Specialization ["Statistics with Python"](https://www.coursera.org/specializations/statistics-with-python), from the University of Michingan, hosted by Dr. Brenda Gunderson.
The Specialization is divided in three courses and each of one has a subfolder with the course notes:
1. [Understanding and Visualizing Data with Python](https://www.coursera.org/learn/understanding-visualization-data?specialization=statistics-with-python): [`01_Visualization`](01_Visualization/Statistics_Python_1_Visuaization.md).
3. [Inferential Statistical Analysis with Python](https://www.coursera.org/learn/inferential-statistical-analysis-python?specialization=statistics-with-python): [`02_Inference`](02_Inference/Statistics_Python_2_Inference.md).
4. [Fitting Statistical Models to Data with Python](https://www.coursera.org/learn/fitting-statistical-models-data-python?specialization=statistics-with-python): [`03_Fitting_Models`](03_Fitting_Models/Statistics_Python_3_FittingModels.md).
The folder `utils` contains some cheatsheets from [Datacamp](https://www.datacamp.com), not pushed to the repository, by accessible from here:
- [Cheatsheet for Numpy](https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.AK5ZBgE)
- [Cheatsheet for Datawrangling](https://www.datacamp.com/community/blog/pandas-cheat-sheet-python#gs.HPFoRIc)
- [Cheatsheet for Pandas](https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.oundfxM)
- [Cheatsheet for SciPy](https://www.datacamp.com/community/blog/python-scipy-cheat-sheet#gs.JDSg3OI)
- [Cheatsheet for Matplotlib](https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet#gs.uEKySpY)
In my case, the Coursera Specialization did not bring that much to me; I learned how to apply some statistical data analysis with pandas and python, since I had more experience with R, regarding statistics. **As far as the theory is concerned, I think that my notes contained in the following file are much more useful**:
[`Statistical_Analysis_Notes.pdf`](Statistical_Analysis_Notes.pdf)
#### TODOs
- [x] Move my old notes on statistics to here.
- [ ] Add a summary notebook on statistics based on these two Medium articles
- [Statistics 1](https://medium.com/@jonathan-hui/statistics-i-in-data-science-machine-learning-40444379dd43)
- [Statistics 2](https://jonathan-hui.medium.com/statistics-ii-in-data-science-machine-learning-d3daad84dae4)
Mikel Sagardia, 2022.
No guarantees.