DS4B 101-P is an introductory-to-intermediate course designed for aspiring data scientists, analysts, and automation engineers who want to move beyond one-off scripts and manual reporting. This course teaches you how to use Python to automate repetitive data tasks, build reusable data pipelines, and integrate data science workflows into business processes.
You’ll learn how to write clean, efficient Python code that not only analyzes data but also automates the extraction, transformation, loading (ETL), reporting, and file management tasks that consume up to 80% of a data professional’s time.
If you want, I can:
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Here’s a professional course write-up for DS4B 101-P: Python for Data Science Automation, suitable for a syllabus, course catalog, or learning platform. DS4B 101-P- Python for Data Science Automation
| Module | Title | Key Automation Topic |
|--------|-------|----------------------|
| 1 | Automating File & Folder Operations | pathlib, batch renaming, folder monitoring |
| 2 | Data Extraction Automation | Reading multiple files, API polling, database queries |
| 3 | Clean Data Pipelines | Writing reusable pandas transforms, handling missing data |
| 4 | Automated Reporting I | Excel and CSV exports with formatting |
| 5 | Automated Reporting II | PDF and HTML reports with templates |
| 6 | Scheduling & Script Execution | Cron, Task Scheduler, schedule library |
| 7 | Error Handling & Logging | Making scripts fault-tolerant and auditable |
| 8 | Integration Mini-Project | Full automation pipeline + basic ML forecast output |
The term "Data Science" has become saturated. Everyone lists Pandas and Scikit-learn on their LinkedIn. But very few people can answer "yes" to the following interview question: If you want, I can:
"Imagine our server receives a new batch of data every night at 3 AM. Write a script that detects the new file, cleans it, merges it with a master table, retrains a random forest model, and sends a Slack alert if the accuracy drops below 80%."
DS4B 101-P trains you for that exact question. (Optionally invoke related search suggestions now
Companies are drowning in data but starving for automation. If a data scientist costs $120k/year, but they spend 20 hours a week doing manual reporting, the company is losing $60k in wasted potential. By taking DS4B 101-P, you position yourself as the person who eliminates the drudgery.