Jupyter Notebooks on the Internet

Copy of Original Article by “Romeo Kienzler” in IBM Data Science Course

There are thousands of interesting jupyter notebooks available on the internet for you to learn from. One of the best sources is: https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks

It is important to notice that you can download such notebooks to your local computer or import them to a cloud based notebook tool so that you can rerun, modify and follow along what’s explained in the notebook.

Very often jupyter notebook are already shared in a rendered view. This means, that you can look at them as if they were running locally on you machine. But sometimes, folks only share a link to the jupyter file (which you can make out by the *.ipynb extention). In this case you can just grab the URL to that file and past it to the NB-Viewer => https://nbviewer.jupyter.org/

The list above gives you a very nice start with a huge collection of materials to explore. Therefore it’s maybe more useful to give you some pointers to interesting notebooks. As we have covered some toy examples with toy data in the labs, let me just point to some work which uses these data and goes further down the road of data science. In addition, as we’ve covered the different tasks in data science we’ll also provide an exemplar notebook for each of those.

First you start with exploratory data analysis, so this notebook is highly recommended to have a look at: https://nbviewer.jupyter.org/github/Tanu-N-Prabhu/Python/blob/master/Exploratory_data_Analysis.ipynb

For data integration / cleansing at a smaller scale, the python library pandas is often used. Please have a look at this notebook: https://towardsdatascience.com/data-cleaning-with-python-using-pandas-library-c6f4a68ea8eb

If you want to already experience what clustering is, have a look at this: https://nbviewer.jupyter.org/github/temporaer/tutorial_ml_gkbionics/blob/master/2%20-%20KMeans.ipynb

And finally, if you want to go for a more in-depth notebook on the iris dataset have a look here: https://www.kaggle.com/lalitharajesh/iris-dataset-exploratory-data-analysis

“I have just kept a copy of this page on my website as a personal note that this infomation is useful and I should read the individual links in my free time.” – Tapish Dongre

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