Python Seminar Course at UC Berkeley (AY 250)
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Python Computing for Data Science

An Upper-Division Undergraduate/Graduate Seminar Course at UC Berkeley (AY 250)

Campbell Hall: Friday 1 - 4 PM FALL 2016


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

Course Schedule

Date Content Reading Leader
Aug 26 Advanced Python Language Concepts (decorators, OrderedDict,
Generators, Iterables, Context Managers)
- scipy §2.1
Sep 2 Pandas, Scipy, & Numpy - scipy §§ 1.3, 1.5, 2.2
- numpy
- skim chap 4/5 of McKinney
Sep 9 Data vizualization (Matplotlib, Bokeh, Altair, Plotly, mayavi) - Skim Tufte's Vizualization book
- colormap talk (Scipy 2015)
Sep 16 Interacting with the world (requests, email, IoT/pyserial) None Josh
Sep 23 Parallelism (asyncio, dask, IPython cluster) - ipyparallel docs Josh
Sep 30 Database interaction (sqlite, postgres, SQLAlchemy, peewee),
Large datasets (xarray, HDF5)
None Josh
Oct 7 Web frameworks & RESTful APIs, Flask TBD Josh
Oct 14 Machine Learning I (sklearn, NLP) TBD Josh
Oct 21 Machine Learning II (keras [tensorflow]) TBD Josh
Oct 28 Image processing (OpenCV, skimage) TBD Stefan van der Walt
Nov 4 Bayesian programming & Symbolic math TBD Brett Naul
Nov 11 holiday
Nov 18 Computational Frameworks (Docker, AWS, Azure, AWS-Lambda) TBD Josh
Nov 25 holiday
Dec 2 Speeding it up (Numba, Cython, wrapping legacy code) TBD Josh
Dec 5/Onward final project work

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


Each Friday 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.


Email us at or contact the professor directly ( You can also contact the GSI, Adam, at ( Auditing is not permitted by the University but those wishing to sit in on a class or two should contact the professor before attending.