https://github.com/fonnesbeck/enar_2019_tutorial

A Primer on Python for Statistical Programming and Data Science
https://github.com/fonnesbeck/enar_2019_tutorial

Last synced: 28 days ago
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A Primer on Python for Statistical Programming and Data Science

• Host: GitHub
• URL: https://github.com/fonnesbeck/enar_2019_tutorial
• Owner: fonnesbeck
• License: mit
• Created: 2019-02-17T14:59:48.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-03-26T23:15:30.000Z (over 5 years ago)
• Last Synced: 2023-03-12T03:52:59.870Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 7.73 MB
• Stars: 26
• Watchers: 3
• Forks: 11
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# ENAR 2019 Tutorial

## A Primer on Python for Statistical Programming and Data Science

**Christopher Fonnesbeck** Senior Quantitative Analyst, New York Yankees

Though Python is ostensibly a general-purpose programming language, it has quickly become a dominant language for machine learning and data science applications. This is due in part to its fundamental strengths as a high-level language, and in part to the powerful set of third-party packages that comprise the Python “scientific stack”. In this hands-on tutorial, we will first cover the fundamentals of Python programming, including data structures, control flow, functions, and classes, with particular attention paid to aspects of the language that is idiomatic. The second part of the course will comprise a survey of Python libraries that are relevant for modern data analysis, particularly in the context of data science and probabilistic programming. These include: NumPy, SciPy, Jupyter, pandas, dask, scikit-learn, PyMC3, matplotlib, Seaborn, and TensorFlow. Demonstrations will be motivated with real-data examples, using Jupyter notebooks to allow for interaction and experimentation.

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fonnesbeck/enar_2019_tutorial/master)

## Tutorial Outline

Tuesday, March 26, 1:45 - 3:30PM

- Intro to Python

- Interactive Computing: Jupyter

- Data Processing: pandas

- Machine Learning: scikit-learn

- Bayesian Statistics: PyMC3