This is the collection of notebooks from a Course on Python for Data Science and Machine Learning by Jose Portilla on Udemy.
Along with the notebooks from the course, I've also added additional Practice notebooks, Exercise links, Cheatsheets that help you hone your skills ✨.
All the files in this repository require Jupyter installed on your computer to run them. However, you can use the same code and try it out in any of the IDE's available. The notebooks are created using the Latest version of Python 3 through the Anaconda distribution.
- Install Python and Jupyter using Anaconda Distribution, which includes Python, the Jupyter Notebook, and other commonly used packages for scientific computing and data science.
Congratulations, you have installed Jupyter Notebook! To run the notebook, run the following command at the Terminal (Mac/Linux) or Command Prompt (Windows):
jupyter notebook
As an existing or experienced Python user, you may wish to install Jupyter using Python’s package manager, pip, instead of Anaconda.
- If you have Python 3 installed (which is recommended):
python3 -m pip install --upgrade pip
python3 -m pip install jupyter
- If you have Python 2 installed:
python -m pip install --upgrade pip
python -m pip install jupyter
Congratulations, you have installed Jupyter Notebook! To run the notebook, run the following command at the Terminal (Mac/Linux) or Command Prompt (Windows):
jupyter notebook
- Practice Python : https://www.hackerrank.com/domains/python/py-introduction
- Simplilearn : https://www.simplilearn.com/data-science-python-exam-free-practice-test
- Hackerrank : https://www.hackerrank.com/domains/python/numpy
- Numpy 100 : https://github.com/rougier/numpy-100/blob/master/100%20Numpy%20exercises.ipynb
- MachineLearningPlus : https://www.machinelearningplus.com/python/101-pandas-exercises-python/
- Exercises from @guipsamora : https://github.com/guipsamora/pandas_exercises
Happy Data Hours 📈