Most self-taught Pythonistas skip logging. DS4B 101-P dedicates serious time to it. You learn to set up logging systems that tell you why a script failed at 2:00 AM. You learn to write scripts that catch errors, retry failed API calls, and save "checkpoints" so you don’t have to start processing from scratch when something breaks.
The traditional data science workflow is often fragmented and manual. A typical analyst might write a linear Jupyter Notebook to clean a CSV file, engineer a few features, and generate a chart. While functional, this approach is brittle; it breaks when the data source changes, is non-repeatable, and cannot be scheduled. DS4B 101-P confronts this fragility by instilling a philosophy of . The course moves beyond the interactive shell, teaching students to view their code not as a one-time experiment, but as a long-term asset. This shift in perspective—from ad-hoc scripting to systematic engineering—is the foundational lesson of the program. DS4B 101-P- Python for Data Science Automation
This draft summarizes the core objectives and technical workflow of the course, designed by Matt Dancho at Business Science University . Course Overview: DS4B 101-P Python for Data Science Automation 1. Objective Most self-taught Pythonistas skip logging
This outline provides a comprehensive introduction to Python for data science automation, covering essential libraries, data manipulation, visualization, and automation techniques. The course is designed to be hands-on, with a focus on practical applications and project-based learning. You learn to write scripts that catch errors,
import pandas as pd import glob
Week 0 — Pre-course setup (self-paced)
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