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This course advances the design of data modeling as it applies to the field of data science while leveraging key concepts from AI, machine learning, and statistics. Data modeling is a combination of various fields which allow the processing of various data types, and representing the data in an expressive way that shows the relationships between data points and intrinsic patterns. The course will show how to identify, design, and implement the modeling process by outlining the framework, determining the appropriate model type, evaluating the model, and representing the outputs in an explainable way. The models used will be based on intelligent algorithms (reasoning, optimization, and pattern recognition), machine learning algorithms (supervised and unsupervised), and statistical methods (descriptive statistics, inferential statistics, multi-variate, and regression). The focus will be developing and applying models using Python-based frameworks to datasets from online resources such as Kaggle, Data.gov, and open-source repositories.