- Working with Jupyter notebooks
- Using code cells
- Extensions to the Python language
- Markdown cells
- Editing notebooks
- NumPy basics
- Broadcasting, array operations, and ufuncs
- Folium and Geo
- Machine learning with scikit-learn
- Plotting with matplotlib and bokeh
- Branching into Numba, Cython, deep learning, and NLP
Skill Level Intermediate
- [Miki] From business, to healthcare, to research, data science is transforming the modern landscape by providing insights that have never been available to us before. With a Python Scientific Stack, you can easily and effectively begin mining your own data. You don't have to be an expert level programmer or mathematician to get great results. Hi. I'm Miki Tebeka, and I've been working with Python and what's now called data science for close to 20 years. In this course, we'll cover the tools and techniques for processing data with the Python Scientific Stack, including pandas for data crunching, Matplotlib for data visualization, Scikit-learn for machine learning, Numpy for numeric computation, and much more.
Okay. Enough talking. Let's process some data.
Python: Data Analysis (2015)with Michele Vallisneri2h 16m Intermediate
NumPy Data Science Essential Trainingwith Charles Kelly3h 54m Intermediate
1. Scientific Python Overview
2. The Jupyter Notebook
3. NumPy Basics
Manage environments5m 11s
6. Folium and Geo
7. NY Taxi Data
10. Other Packages
11. Development Process
Next steps1m 33s
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