Ds4b 101-p- Python For Data Science Automation |top| Jun 2026

Learn to use VS Code as your Python development environment.

: Filtering, grouping, and joining data using the Pandas library .

Setting up scripts that load the latest production data, apply the pre-trained model, and output predictions directly back to a database. DS4B 101-P- Python for Data Science Automation

The curriculum is crafted for a specific audience, including:

One of the standout features of the curriculum is its practical approach to the data pipeline. The course typically centers around a realistic business case, such as sales forecasting or financial reporting. Through this lens, students learn the "dirty work" of data science that is often glossed over in academic settings: data collection, cleaning, and transformation. By mastering libraries like Pandas for data manipulation and Plotly for interactive visualization within an automated context, students learn to build reports that update themselves. This eliminates the "Excel hell" of copy-pasting data, ensuring that insights are delivered faster and with higher accuracy. Learn to use VS Code as your Python development environment

Keep database credentials, API keys, and file paths out of your main code logic. Use environment variables or local .env files managed by libraries like python-dotenv to safeguard sensitive data.

Week 0 — Pre-course setup (self-paced) The curriculum is crafted for a specific audience,

Before automation can begin, data collection must be touchless. The automation pipeline leverages Python to communicate directly with corporate infrastructure:

Exploiting Pandas internals to process millions of rows simultaneously.

To run Python scripts on a recurring schedule. Mac Automator: Equivalent scheduling for macOS users.