- [Miki] Now that we've covered the basics of working…with a python scientific stack,…here are some ideas on how to become more proficient…with data processing.…The best way forward is to find a data project to work on.…The good news is that there is a lot of…readily available data out there.…It can be a personal project,…like analyzing statistics of your favorite sports team.…Your can look for insights in your own finances,…and maybe even save some money in the process.…You can also find projects in your work space.…A lot of companies have data just laying around.…
And if you don't have any idea,…you can always head over to Kaggle.…They host data science competitions.…And some of them even award serious cash prizes.…Get engaged with the community.…There are many meet-ups and conventions.…From the big Pydata conventions,…to local meet-ups in your area.…Python community is friendly, knowledgeable,…and willing to help.…Or you can find an online community as well.…There are a lot of learning materials out there.…I highly recommend the book Python for Data Analysis…
- 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
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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