profjsb / Python Seminar
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Python Computing for Data Science
A Graduate Seminar Course at UC Berkeley (AY 250)
Campbell Hall: Thurs 3:30 - 6:30 PM FALL 2020
Synopsis
Python has become the de facto superglue language for modern scientific computing. In this course we will learn Pythonic interactions with databases, imaging processing, advanced statistical and numerical packages, web frameworks, machine-learning, and parallelism. Each week will involve lectures and coding projects. In the final project, students will build a working codebase useful for their own research domain.
This class is for any student working in a quantitative discipline and with familiarity with Python. Those who completed the Python Bootcamp or equivalent will be eligible. You should follow the steps to install the Anaconda 3.6.X distribution as well as git.
Course Schedule
| Date | Content | Reading | Leader |
|---|---|---|---|
| TBD |
Advanced Python Language Concepts (decorators, OrderedDict, Generators, Iterables, Context Managers)
|
- GIT - scipy §2.1 |
Josh |
| TBD |
Pandas, Scipy, & Numpy Numpy:
|
- scipy §§ 1.3, 1.5, 2.2 - numpy - skim chap 4/5 of McKinney |
Josh |
| TBD | Data vizualization (Matplotlib, Bokeh, Altair, Plotly, mayavi) | - Skim Tufte's Vizualization book - colormap talk (Scipy 2015) |
Josh |
| TBD | Interacting with the world (requests, email, IoT/pyserial) | None | Josh |
| TBD | Holiday (no class) | ||
| TBD | Parallelism (asyncio, dask, IPython cluster) | - [ipyparallel docs] (http://ipyparallel.readthedocs.io/en/latest/intro.html) | Josh |
| TBD |
Database interaction (sqlite, postgres, SQLAlchemy, peewee), Large datasets (xarray, HDF5) |
None | Josh |
| TBD |
Machine Learning I (sklearn, NLP) NOTE: 3:10pm start! |
None | Josh |
| TBD | Machine Learning II (keras [tensorflow]) | None | Josh |
| TBD | Spring Break | ||
| TBD | Image processing (OpenCV, skimage) | None | Stefan van der Walt |
| TBD | Web frameworks & RESTful APIs, Flask | None | Josh |
| TBD | Bayesian programming & Symbolic math |
Probabalistic Programming eBook install: pip install pymc3 |
TBD |
| TBD | Speeding it up (Numba, Cython, wrapping legacy code) | TBD | Josh |
| TBD | final project work | ||
| Onward |
Useful Books
- Elegant Scipy (UC Berkeley Library link)
Sidebar Concepts
Throughout these lectures we will be peppering in sidebar knowledge concepts:
- Jupyter & JuypterLab
- using git & github
- Docker
- Data science workflows
- reproducible research
- application building
- debugging
- testing
Workflow
Each Monday we will be introducing a resonably self-contained topic with two back-to-back lectures. In between a short (~20 minute) breakout coding session will be conducted. Homeworks will require you to write a large (several hundred line) codebase.
Help sessions will be conducted interactively on the Piazza site for the course. There is also an in-person help session every Tuesday from 11am-noon at BIDS (in Doe library). Email Josh with any questions.
Contact
Email us at [email protected] or contact the professor directly ([email protected]). You can also contact the GSI, Chelsea Harris, at ([email protected]. Auditing is not permitted by the University but those wishing to sit in on a class or two should contact the professor before attending.