Python for Data Processing and Office Automation

Most weeks, somewhere on your computer, the same half-hour happens. The email arrives, the attachment opens, you trim the dead rows, fix the dates that came through as text, paste it next to last week's, run the pivot, copy the totals into the deck, hit send. Nothing about it is hard. It just never *stops*. This course is for the people who do that half-hour — analysts, ops, researchers, the unofficial spreadsheet-keeper of every team — and have decided it would be nicer if a computer did it instead. You will not become a software engineer. You will learn enough Python, enough pandas, and enough of the boring useful libraries (openpyxl for Excel, python-docx for Word, pypdf for PDFs, requests for APIs, smtplib for email) to take that recurring chore and shrink it to a single command. The course assumes you have never written a line of code, and it gets you to a working script faster than you'd expect — pandas reads your CSV in lesson 16, not lesson 60. A working script is only half the job. The other half is the unglamorous part nobody warns beginners about: telling the difference between data and "data" (the string `"$1,200.50"` is not a number); never hard-coding the password you'd be fired for emailing; making the file-renamer print what it *would* do before it does it; logs your future self can actually read at 7am when the scheduled run failed. You'll get those habits, one at a time, until they feel obvious. In 2026, most people in this audience will have an AI assistant draft the first version of the script. That is fine — this course teaches the one thing the assistant can't do for you: read the script it just produced, run it, and tell whether it works. By the end, you can.