Introduction to Programming and Plotting with Python

This comprehensive 3-day workshop introduces researchers to Python programming through practical, hands-on exercises using Google Colab. No prior programming experience is required - we start from the absolute basics and build up to real-world data analysis skills.

You'll learn essential Python concepts including variables, data structures, control flow, and functions, then apply these skills to analyze and visualize research data using pandas and matplotlib. The workshop emphasizes reproducible workflows and collaborative coding practices essential for modern research.

By the end of this workshop, you'll be comfortable working with Python notebooks, manipulating datasets, creating publication-quality plots, and sharing your code through GitHub. All materials and datasets are provided, and you'll leave with practical skills you can immediately apply to your own research projects.

Format: Each day is approximately 4 hours with hands-on exercises using Google Colab

Prerequisites: No prior programming experience required. You'll need a Google account to access Google Colab and basic computer literacy

What you'll learn: Core Python programming concepts, data manipulation with pandas, creating publication-quality plots, and sharing your work through GitHub for reproducible research

Materials provided: All datasets, code templates, and resources will be provided. You'll have lifetime access to all workshop materials

Day 1: Python Fundamentals

Learn core Python concepts using Google Colab

Welcome & Setup (30 min): Workshop overview and Google Colab introduction - notebooks, markdown, and saving to Drive

Basic Syntax & Variables (45 min): Variables, data types, expressions, printing, string formatting, and clean code practices

Data Structures (1 hr): Lists (indexing, slicing, methods), dictionaries (key-value pairs, nesting, looping), plus brief intro to sets and tuples

Control Flow (1 hr): Conditional statements (if, elif, else) and loops (for, while) including iteration over lists and dictionaries

Functions (45 min): Defining and calling functions, arguments, return values, scope, default arguments, and documentation with docstrings

Day 2: Data Analysis with Pandas

Learn file I/O and Pandas for research data exploration

Review & File I/O (1 hr): Recap Day 1 concepts, reading/writing files, and CSV handling

DataFrames 101 (1 hr): Loading datasets, inspection methods, selecting columns and rows, and filtering with conditions

Data Manipulation (1 hr): Creating new columns, handling missing data, sorting and grouping, and aggregations

Basic Plotting (45 min): Line, bar, scatter, and histogram plots with pandas + matplotlib. Plot customization (labels, titles, colors) and quick intro to seaborn for polished plots

Day 3: Advanced Plotting & GitHub

Master publication-quality plots and learn to share your research code

Advanced Plotting (1 hr): Multiple plots and subplots, exporting figures for publications, and group exercise recreating a scientific figure

GitHub for Researchers (2 hrs): Introduction to version control for reproducible research, hands-on GitHub account and repository creation, uploading notebooks with datasets and documentation, adding licenses and README files

Final Mini-Project (45 min): Analyze and visualize a complete dataset in Colab, push your work to GitHub with clear documentation, and share your results with the group

Showcase & Wrap-up (30 min): Optional project demonstrations, final Q&A session, and resources for continued learning