This intermediate path covers the core Python tools and workflows used in applied data science. You will work with NumPy and Pandas for numerical computing and data manipulation, then use datasets such as Titanic, housing, Iris, and airline data to practice analysis in realistic contexts. The path introduces essential steps in the data science process, including cleaning messy data, engineering features, creating visualizations, and preparing datasets for modeling. You will also explore supervised learning with regression models and unsupervised learning with clustering and dimensionality reduction. By the end, you will have practiced moving from raw data to insight using Python. This path is suited for learners who have some programming background and want to strengthen their ability to analyze data and apply foundational machine learning techniques.
