Data is often messy, requiring cleaning and restructuring before it can be reliably used in a program or project. In this course, learners will augment their understanding of Python using two of the most popular libraries for data cleaning and wrangling, NumPy and Pandas. First, learners will practice working with NumPy objects, transforming data into efficient arrays for ease of analysis. Then, learners will clean and structure arrays into readable tabular DataFrames using Pandas, allowing them to profile a dataset for key answers and values.

Decision tree models are classification algorithms that sort novel data into categories based on iterative splitting, like the branches of a tree, according to input parameters. In this course, learners will identify use cases for decision trees in Python. They will wrangle data and implement a decision tree model before attempting to evaluate its effectiveness. Finally, learners will use their knowledge of the mathematics behind decision trees to tune the model and improve its classificatory function.