Data Science Project Management with CRISP-DM

Successfully leading a data science project requires more than just writing machine learning code; it demands a structured, repeatable methodology to align technical efforts with business value. This text-based course guides you through the entire lifecycle of a data science project using CRISP-DM, the industry's most trusted project management framework. You will transition from simply writing algorithms to strategically managing data initiatives, ensuring every phase—from initial business alignment to final deployment—is executed systematically. You will learn how to collaborate with stakeholders, define clear success criteria, structure data preparation, and manage modern deployment cycles to deliver measurable business outcomes. What you'll learn: - Understand the six core phases of the CRISP-DM methodology and how they map to modern project management workflows. - Define clear business goals and translate them into actionable data mining success criteria. - Manage data acquisition, exploration, and foundational data governance and privacy requirements. - Structure data preparation steps systematically to ensure high-quality inputs for machine learning models. - Evaluate model performance against both technical metrics and real-world business objectives. - Plan successful deployments, including modern MLOps monitoring considerations, model maintenance, and documentation. Starting with foundational definitions and project setup, the course guides you step-by-step through each phase of the methodology with practical scenarios and structured written walkthroughs. This course is designed for aspiring data science project managers, business analysts, and entry-level data professionals looking to structure their workflows. No prior programming or advanced technical experience is required. Start reading today to master the structured approach to delivering impactful data science projects.