Introduction to Programming in Python

EOAS Workshop

When: 3-6pm on Tues Oct 9 & Wed Oct 10

(the workshop is 6 hours in total, split into two afternoon sessions)

Where: ESB 5104-5106

Instructor: Jennifer Walker (jwalker@eoas.ubc.ca)

This workshop is an introduction to Python programming for scientific computing and research. Participants will learn basic programming concepts and syntax, gain a broad understanding of Python’s rich ecosystem of scientific tools, and develop skills in data management and analysis using real-world scientific data.

The curriculum is designed for incoming graduate students with no previous programming experience. Any other students, staff, or faculty in the department who would like to learn about Python are also welcome to attend.

Slides

You can view the workshop slides online at bit.ly/eoas-python-slides.

Setup

You’ll want to bring your laptop for lots of hands-on practice as we work through the lessons and exercises. Please make sure to download and install the required software on your laptop prior to the workshop. For setup instructions, please click here.

Learning Goals

By the end of this workshop, participants will be able to:

• Explain what programming is and identify possible uses for programming in their research.
• Describe the Jupyter notebook structure and identify ways it can help make scientific research transparent and reproducible.
• Create a Jupyter notebook including narrative text, equations, Python code, and graphs.
• Identify the major data types in Python, work with variables, and perform basic mathematical operations.
• Use loops and conditionals to automate tasks and control program flow
• Explain what libraries are and identify some of the main libraries in the Python scientific ecosystem.
• Use functions and data structures from the Pandas, Matplotlib, and Cartopy libraries for exploratory data analysis and visualization, including:
  • Read data from a CSV file and compute simple summary statistics and other calculations.
  • Extract subsets of a dataset using various indexing methods.
  • Use techniques such as sorting, filtering, and aggregation to explore a dataset.
  • Visualize data in graphs.
  • Plot geospatial data on a map.

Credits

Some portions of this workshop are adapted from or inspired by the following instructional materials:

• Data Insights with Python for Beginners (Copyright Ladies Learning Code

  CC BY 4.0 license)

• U of T Coders Data Carpentry Workshop (Copyright U of T Coders / Software Carpentry

  CC BY 4.0 license)

• U of T Coders Cartography and Mapping Lesson (Copyright U of T Coders / Mozilla Science Lab

  Apache License 2.0)

• Python for Ecologists (Copyright Software Carpentry

  CC BY 4.0 license)