Research Design and Applications for Data and Analysis for Early Career Data Scientists

Introduces the data sciences landscape, with a focus on learning data science techniques to uncover and answer questions students will encounter in industry. Lectures, readings, discussions, and assignments will teach how to apply methods to ask better questions, gather data, interpret results, and convey findings to various audiences. The emphasis is on making practical contributions to real decisions that organizations make.

This 4-credit version of the course is designed for early-career learners in our 5th Year MIDS pathway. It provides additional attention to introducing professional and business knowledge and skills, and providing students with opportunities to apply and reflect on learning how to become a data science professional.

Skill Sets

Research design / Question formulation / Data and decision making / Understanding cognitive bias / Data for persuasion and action / Integrating data and domain knowledge / Storytelling with data

Student Learning Outcomes

By the completion of this course, students will be able to:

• Apply techniques and approaches focused on building work relationships and engaging in interactions that align with organizational goals.
• Assess and select data and the data collection methods that best fit a specific outcome or need.
• Demonstrate an understanding of foundational approaches to project management and strategic thinking by imagining, planning, and designing a data science project from start to finish.
• Identify and describe effective teamwork skills, practices, and characteristics of an effective workplace or project team.
• Justify and defend an analytical approach—descriptive, predictive, or explanatory—to inform efficient decision making.
• Understand and apply successful communication strategies and methods (written, spoken, and visual) for teams and for various stakeholders within an organization with different contextual requirements and expectations, including summarizing and presenting key ideas effectively for various stakeholders.
• Understand key principles that affect human decision-making processes, such as biases and contextual concerns (e.g., ethical and legal) that affect human decision-making processes and apply knowledge of those principles throughout the course to mitigate biases, facilitate better decision making, and improve communication.
• Describe the role that data science as a domain and as a set of practices and processes plays in decision making made by people in organizations, and establish an awareness of common social structures, practices, norms and expectations in data science organizations, teams, and workplaces.