is a specialised, project-based course from Business Science University designed to transform data analysts into automation experts. Unlike generic introductory courses, this program focuses on converting manual, repetitive business processes into robust, Python-based automation workflows. Course Overview and Philosophy
DS4B 101-P: Python для автоматизации обработки данных DS4B 101-P- Python for Data Science Automation
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 is
She wrote a reusable function to strip spaces, convert dates, and flag outliers — all from her automation module. A typical analyst might write a linear Jupyter
: Filtering, grouping, and joining data using the Pandas library .
Furthermore, the course emphasizes the concept of reproducibility, a cornerstone of professional data science. In a manual workflow, if a mistake is found or new data arrives, the entire process must be redone from scratch. DS4B 101-P teaches students how to build automated pipelines that can be rerun with a single command. This includes integrating business logic, such as forecasting with Facebook Prophet, directly into the code. The result is a system that not only analyzes the past but predicts the future, delivering these insights via automated emails or interactive dashboards without human intervention.
: Communicate findings effectively to stakeholders. Key Skills : Interactive plotting with Plotly .