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Stat 159/259 - Reproducible and Collaborative Data Science
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An interactive Git Tutorial: the tool you didn’t know you needed
A quick overview of the Jupyter Notebook and IPython
Reading discussion - Developing open source scientific practice
Reading discussion - Scientific Python, IPython, Jupyter
Class practice: strings, lists & numbers
Conda and pip - managing environments
From September 25 reading
Make: automating tasks
LIGO, the 2017 Nobel prize in physics, and wrapping up Makefiles
An Introduction to the Scientific Python Ecosystem
Motivation: the trapezoidal rule
NumPy arrays: the right data structure for scientific computing
High quality data visualization with Matplotlib
The trapezoidal rule: vectorization and perormance
Matplotlib: Beyond the basics
Matplotlib: Live plots
Matplotlib image tutorial
Multichannel images
Strings
NLTK: Natural Language Made Easy
Data - an introduction to the world of Pandas
Data Manipulation
An introduction to Sphinx
P-values discussion with Prof. Philip B. Stark
Testing your software in Python
Simple chaotic behavior in non-linear systems
IPython: beyond plain Python
A quick overview of the Jupyter Notebook and IPython
Notebook Basics
Markdown Cells
Heading 1
Heading 2
P-values, Probability, Priors, Rabbits, Quantifauxcation, and Cargo-Cult Statistics
Readings
Lab 1
Lab 10
Lab 2
Lab 3
Lab 4
Lab 5
Lab 6
Lab 7
Lab 8
Lab 9
Course resources
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