Python for IASSIST

Introduction

Data professionals supporting researchers provide valuable services throughout the data management life cycle. According to recent surveys, up to 80% of a data scientist’s time can be spent cleaning, harmonizing and integrating data (a.k.a.: data wrangling). While there are many useful tools available to assist with these types of workflows, knowledge of basic programming can be extremely empowering.

This full day workshop will provide an introduction to Python - one of the most popular and versatile languages in use today.

No prior programming experience required! The workshop will be split into two parts: “Basic Python Programming” in the morning, and “Working with Data using Python” in the afternoon.

Date

May 31, 2016 (9:00 AM - 3:30 PM ((w/ lunch)

Location

Computer Lab 205 - Ulrike Pihl’s House https://goo.gl/rrtHq7

Instructor

Tim Dennis, UCSD (@jt14den)

Schedule

1. 9am - noon - Building programs with Python
2. noon-1:30p - Lunch
3. 1:30-3:30p - Pandas, APIs & JSON (Check out the full scrape interwebz github repo from Rochelle Terman)

Other materials

• Etherpad: http://pad.software-carpentry.org/iassist-python
• Notebooks
  • Numpy
  • Loops
  • Lists
  • Files & conditionals
  • Pandas and writing modules

Python Resources

Your library might subscribe to numerous programming e-books.

• Python for Data Analysis - Excellent book written by the guy who created Pandas
• Web Scraping with Python - If you need get data from websites or an API
• Python documentation
• Jupyter Documentation - If you want to get more out of the Jupyter notebooks.
• A gallery of Jupyter (Ipython) Notebooks
• Exercism.io - Download and solve practice problems in nearly 30 different languages including Python

Set up

Sign-up for an account with SageMathCloud using the email you use to register for the workshop with https://cloud.sagemath.com/.

Requirements

Participants are required to abide by a Code of Conduct. We are using the Software Carpentry Code for this workshop.

Goals

Users will be able to:

• proficiently use scientific notebooks in the cloud
• write basic python programs
• operate on csv files
• transform web-fetched json data into csv format
• reference materials

Thanks to SageMathCloud for providing the Jupyter infrastructure: