Data Science for Managers
Description
- Served as a teaching fellow; assisted MBA students with classroom coding exercises.
- Developed course materials, including new case studies, technical notes, and code notebooks students used to analzye case data.
- Developed interactive web applications to illustrate important concepts from statistics and machine learning.
Course Overview:
Over the past decade, numerous firms have extensively invested in developing business infrastructure to collect, store, and analyze data effectively. A wide range of roles across finance, marketing, human resources, operations, innovation, and strategy now rely heavily on data for critical decision-making input and implementation. Indeed, many firms strategically differentiate themselves by their ability to translate their vast amounts of data into meaningful insights that help them gain an edge over their competitors.
The Data Science for Managers (DSM) I course provides students with the necessary foundations to effectively derive and evaluate data-driven insights to inform managerial decisions. In this hands-on course, students will learn how to view and solve business problems from a data perspective. The course will introduce fundamental principles that will enable MBA graduates to understand the opportunities and limitations of analytics, develop a familiarity with programming (using the R language), and build a robust data analytics mindset.
Importantly, students will have opportunities to see how data science is used across a broad range of business environments. The course will focus on managers’ roles in data science projects, including hypothesis generation and testing, model design, interpretation of results, and the formulation of actionable recommendations.
Data science is an interdisciplinary field that combines principles from statistics and computer science with substantive domain knowledge to extract useful insights from data. The tools, technologies, and methodologies employed in data science are numerous; however, they broadly fall into four categories that mimic the typical process flow of a data science project and comprise the course’s four modules.