: Students learn to ingest data from CSVs or databases, clean it, perform analysis, and write results back to a SQL database. Business Transformation
The core philosophy of the course is built upon the "Business Science Problem Framework." This methodology ensures that data science is not performed in a vacuum but is instead aligned with financial goals and operational efficiency. Students are taught to view Python not just as a programming language, but as a robust engine for business transformation. By mastering libraries such as Pandas, Polars, and Plotly, learners gain the ability to manipulate massive datasets and create interactive visualisations that can be deployed across an enterprise. 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. : Students learn to ingest data from CSVs
: Implementing time-series analysis and forecasting using the SQL Integration By mastering libraries such as Pandas, Polars, and