Automate Everyday Tasks with Python

It is for students and freshers who have finished a first Python course, so variables, loops, functions, lists and dictionaries feel comfortable, but who have not yet written a script that reads a folder, edits a spreadsheet, calls a web API or sends an email. It also suits interns and new joiners whose work involves repetitive reports, renaming files by hand or copying data between sheets and websites.

The course does not teach the Python language itself. If loops and functions still feel new, start with Python and come back. If your goal is analysing data rather than automating chores, Data Analytics with Python goes deeper into pandas, statistics and charts; this course uses pandas only for sheets too big to handle cell by cell. Mouse-and-keyboard GUI automation and desktop app building are left out, so every script works through files, the web and the command line.

Each lesson is a short set of reading cards built around one small script or one decision, with diagrams where a picture explains it faster than text. You can read the cards or play the narration and listen. A multiple-choice quiz follows every lesson, and each answer comes with an explanation, so a wrong guess about what a script will do becomes a quick correction rather than a broken folder. If a card or question is still unclear, you can ask the AI guide about it.

Lessons open in order on a course map. The examples use Python 3.12 or newer and standard tools: pathlib and shutil for files, csv and json, openpyxl and pandas for spreadsheets, requests and BeautifulSoup for the web, smtplib for email and argparse for command-line options. Quiz questions ask what a snippet prints, which line fails, which library fits the job, or what a script will do to a folder before you run it.

The last topic pulls everything together. It compares choices that come up again and again, such as copy versus move, requests versus Playwright, cron versus the schedule library and logging versus print, and then walks through complete jobs: a month-end report, a nightly backup folder, a weekly price-scraping job and a batch of invoice PDFs.

Udemy and Coursera teach Python automation mostly through video lectures, where you watch an instructor write a script and then copy it. freeCodeCamp offers articles, long video tutorials and an interactive curriculum. Those are good choices if you like to follow along with a screen recording. This course takes a different format: short reading cards you can get through in a few minutes, and a quiz after every lesson that checks you can predict what a script will do. That suits learners who want to fit practice into a busy day and want to know, lesson by lesson, whether the idea actually stuck.

Start with one task you already do by hand every week, such as renaming downloads or copying numbers from a CSV into a report. Learn the matching lesson, then write the script for your own files. Build in the safety habits from the first topic early: make scripts idempotent so running them twice does no harm, do a dry run that prints what would change before anything moves, and keep paths and settings in a config file instead of hardcoding them.

When you scrape, read the site's robots.txt and terms, keep a delay between requests and prefer an official API when one exists; the course covers this etiquette before it covers browser automation. Once a script works, add logging and error handling, give it command-line arguments, and schedule it with cron, Windows Task Scheduler or the schedule library so it runs without you. Finally, use the quiz as active recall: answer before you look, and return to the lessons where you got questions wrong. Pulling an answer from memory makes it stick better than reading the same card again.