Welcome to Data Science for Managers!¶
Note: This site is currently in public beta mode. Therefore, some content may be a work-in-progress. If you encounter any issues using the site, please contact mparzen@hbs.edu and phamilton@hbs.edu.
“Can you imagine a CFO going to the CEO and saying, ‘I don’t really know how to read a balance sheet, but I have someone on my team who is really good at it.’ We would laugh that person out of the room. … And yet I know a whole bunch of people in other disciplines [… who] without blinking an eye, would go to the CEO and say, ‘This analytics stuff is complicated. I don’t have a full grasp on it. But I have assembled a crackerjack analytics team that is going to push us to the next level.’ I think this is an answer that is no longer acceptable.”
Florian Zettelmeyer, Academic Director, Kellogg’s Executive Education Leading with Advanced Analytics and Artificial Intelligence Program.
Data science has become the new language of business. Many roles across the enterprise in finance, marketing, human resources, operations, innovation, and strategy now rely heavily on data science for critical decision-making input and implementation. Given the increasing ubiquity and need for analytics across organizations, it is imperative that MBA students learn key concepts, understand the opportunities and limitations of analytics, and develop a solid grasp of the tools that are currently being deployed. The Data Science for Managers (DSM) course aims to provide the general manager with the necessary tools to develop a data science organization, evaluate data-driven insights, and productively collaborate with data analysts, engineers, and scientists.
Note
There is no required background for this course! DSM is designed for poets and quants alike.
Students completing the DSM course are expected to be able to combine their proficiency in data analytics with their managerial abilities to identify business opportunities, frame problems, shape solutions, and lead change in organizations. The DSM course will also prepare students for a range of EC courses focusing on different types of analytical approaches and application domains. DSM introduces students to the types of data analytics currently used to drive key decisions in a variety of business and organizational contexts. In the course, students will learn:
Foundational statistical techniques most frequently used by data scientists for modeling, prediction, and inference,
Basic coding for data manipulation, visualization, and analysis using R, the leading open-source statistical programming language, and
The range of considerations driving data science strategy in organizations, including how to set up a data infrastructure and experimentation platform, how to embed data scientists in organizations, and how to manage data policies related to privacy and fairness.
Importantly, students will have opportunities to see how data science is used across a broad range of business environments and applications. The course will focus on business applications, including managers’ roles in telling stories with data, hypothesis generation and testing, model design, interpretation of results, and formulation of actionable recommendations. The course will also cover important managerial questions about when and how to set up – and how to manage – a data science organization, selection of metrics for project evaluation, data storage and governance, and some advanced topics such as algorithmic fairness, machine learning interpretability, and building and managing a data dashboard.