SEIU Python Bootcamp

Day 1 is all about getting comfortable with the language itself. Before Python can do anything useful for us — pull data, clean a spreadsheet, or automate a report — we need to understand how to talk to it.

We'll work through an interactive Jupyter notebook in Google Colab and cover the building blocks every Python program is made of:

• Variables and data types — how Python stores numbers, text, booleans, and None.
• Operators — arithmetic, comparison, and logical operations you'll use constantly.
• Control flow — using if/elif/else and for/while loops to make decisions and repeat work.
• Core data structures — lists, tuples, and dictionaries for grouping related values.

By the end of the session you'll be able to read a small Python program, predict what it does, and write short scripts of your own. Don't worry about memorizing syntax — the goal today is to build intuition. Everything we learn here is what the next two days are built on top of.

On Day 1 we learned the syntax of Python. Today we give that syntax legs.

So far we've been running small snippets inside notebook cells. Real-world Python usually lives in files and scripts — reusable programs you can run again and again, from a terminal, on a schedule, or as part of a bigger workflow. In this session we'll take everything you already know and package it into code that does actual work.

We'll cover:

• Functions — bundling logic into reusable, named pieces you can call whenever you need them.
• Scripts — writing standalone .py files and running them from the command line.
• The terminal — a quick, friendly tour of the commands you'll use to navigate folders, clone repos, and run your scripts.
• Files and I/O — reading data from files and writing results back out.
• Modules and imports — pulling in extra functionality (like datetime or requests) from Python's ecosystem.

You'll build a small end-to-end script that ties these ideas together, so by the end of the day you'll be able to write Python that lives outside a notebook and behaves like a real tool.

Welcome to the last day of the bootcamp. Over the first two days you learned how to write Python and how to organize it into scripts. Today we'll point those skills at one of the most common — and most valuable — jobs in the real world: cleaning messy data.

We'll work in a Colab notebook with a realistic (and intentionally messy) dataset of workshop registration records. Along the way you'll learn to:

• Load and explore data with Pandas DataFrames and basic exploratory data analysis (EDA).
• Spot data quality issues — missing values, wrong data types, duplicates, inconsistent formatting, and sentinel values like -999.
• Handle missing data thoughtfully, choosing the right strategy for each column instead of blindly dropping rows.
• Deduplicate records using composite keys built from multiple fields.
• Fix data types so that things like ZIP codes and phone numbers survive the cleaning process intact.
• Use practical Pandas methods — .info(), .fillna(), .astype(), .drop_duplicates(), .groupby(), and boolean indexing.

We'll wrap up with a short guided project where you put it all together on your own. By the end of today you'll have hands-on experience with the same cleaning workflow data professionals use every day — and a template you can reuse on your own datasets long after the bootcamp ends.