An Analyst’s Guide to Business Analytics and Machine Learning with AI: Applying the Business Analytics Process with Python
Mark Keith, PhD
This resource teaches students to build, evaluate, and communicate analytical results in Python, the most widely used language for analytics and machine learning in practice. Framing data literacy as the new spreadsheet literacy, the course takes students from Python fundamentals through the CRISP-DM framework, covering data cleaning, feature engineering, regression, and classification, while integrating AI-assisted coding and analytics workflows throughout as a core professional skill. Students finish with model deployment, analytics strategy and governance, and responsible analytics practices, then apply everything in an end-to-end analytics pipeline project and technical interview preparation.
Course Objective
This resource teaches students the latest data mining techniques, including descriptive and predictive analytics, dashboard design with Tableau, data storytelling, Excel regression methods, and advanced algorithms using Microsoft Azure Machine Learning Studio.
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