Python Intermediate – Data Analysis & Engineering

This intermediate-level course is designed for professionals who want to use Python to work with real-world data, automate data preparation, and build predictive workflows that support decision-making. You will learn how to import datasets from multiple sources, clean and transform raw data, and apply rigorous exploratory data analysis to uncover trends and insights.

The course emphasizes data engineering practices: handling missing values, transforming formats, normalizing features, binning, encoding categories, and organizing dataframes into efficient processing pipelines. You will also learn to generate visual insights using Python’s most powerful analytical libraries.

As you advance, you will explore machine learning fundamentals and learn to build and evaluate regression models with Scikit-learn. You will train linear, multiple, and polynomial regression models, assess performance using standard metrics, and apply feature engineering to improve accuracy.

Through hands-on labs and real datasets, you will gain practical experience with the full data analysis workflow—from ingestion to model evaluation—building skills that directly apply to analytics, AI projects, and data-driven environments.