Data Science 210A
Capstone for Early Career Data Scientists
In the Capstone class, students combine technical, analytical, interpretive, problem-solving, and strategic thinking dimensions to design and execute a full end-to-end data science project. Students will develop their technical and non-technical skills as data scientists who focus on real-world and impactful applications and situations. The final project provides a learning opportunity and “sandbox” to integrate all skills and concepts learned throughout the MIDS program and provides experience and hands-on tools in formulating and implementing an impactful and compelling project. Students are evaluated on their ability to work in a dynamic team environment to collaborate, co-develop, and communicate their work.
The capstone is completed as a group/team project (3–4 students), and each project will focus on open, pre-existing secondary data. A robust listing of open datasets will be made available before the capstone course begins.
Student Learning Outcomes
- Construct and perform persuasive, informative and understandable written, spoken, or visualized narratives that concisely convey findings, solutions, and applications of data-driven approaches that have been incorporated in project work.
- Demonstrate an ability to integrate and synthesize knowledge and skills gained through other courses in the program (critical technical, analytical, strategic thinking, problem-solving, communication, influencing, and management skills) in developing and implementing a capstone project that addresses a key data science problem.
- Demonstrate proficiency in applying technical and analytical skills towards the collection, storage, and analysis of data towards problem-solving and project execution. Assess and select data and the data collection methods that best fit the specific outcome or need of a project or problem space.
- Demonstrate proficiency in identifying target user audience for the Capstone project and conduct expert and target user interviews to validate problem framing, scoping, and hypothesis.
- Demonstrate proficiency in selecting the appropriate data science and machine learning approaches for a specific project and perform model evaluation to demonstrate the efficacy of the model and its value to the target users.
- Effectively engage in a process of teamwork, feedback from peers, instructors and experts, and informed iteration that mirrors the challenges and opportunities of applying data science in a realistic organizational setting. Conduct self-assessment of professional development and leadership.
- Identify and articulate a problem space to address through application of data driven methods, approaches and practices that include an understanding of stakeholders, social contexts, potential impact, and potential obstacles.
- Identify and describe effective teamwork skills, practices, and characteristics of an effective workplace or project team, including distribution of team tasks and duties.
- Understand and apply successful communication strategies for teams, for various stakeholders within an organization with different contextual requirements and expectations.Understand, incorporate, and practice integrated understanding of what it takes to imagine, design, and execute a data science project from start to finish.
Course must be taken for a letter grade to fulfill degree requirements.