DATASCI 210

Capstone

3 credits · Graduate

Design and execute an end-to-end data science and applied artificial intelligence project, culminating the skills learned through the MIDS program. Serving as a real-world "sandbox," this capstone course integrates the technical capabilities and strategic thinking developed throughout the program. Working in a dynamic team, you will tackle an impactful problem using open, pre-existing data. Beyond modeling, you will refine professional skills by collaborating, co-developing, and communicating findings through formal written and oral presentations. Supplemented by practical discussions and readings, this experience bridges classroom and industry, preparing you for long-term success as a practitioner.

Topics

Analyze a high-impact and compelling problem space and formulate a data-driven approach by evaluating stakeholder needs, social contexts, potential impacts, and implementation challenges. Integrate , synthesize , and apply technical, analytical, strategic, problem-solving, communication, leadership, and management skills acquired throughout the program to design and implement a comprehensive and end-to-end data science capstone project. Collaborate , evaluate feedback , and refine project outcomes through iterative teamwork and engagement with peers, instructors, and industry experts, demonstrating professional practices in organizational settings. Evaluate and select appropriate data sources and implement data collection, data transformation, data engineering, and feature engineering methods that best align with the objectives, constraints, and desired outcomes of a data science and applied AI project. Evaluate , compare , select , and refine appropriate machine learning, statistical, and artificial intelligence models and systems, based on problem requirements, and assess model performance, system outputs, and application results using relevant evaluation strategy, metrics, and validation techniques to ensure reliable and effective solutions. Create and deliver clear, persuasive, and evidence-based and polished written, oral, and visual communications that effectively convey project findings, recommendations, critical thinking, and the value of data-driven solutions to diverse audiences. Apply ethical and privacy preservation considerations and mindset to the design and implementation of data science and applied AI projects.

Official catalogue course objectives and student learning outcomes

Prerequisites & course information

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Sources

Course catalogue ↗2026-2027