Built entirely around Harvard Data Science Review articles
Assessment focuses on engagement and critical thinking
First-of-its-kind course incorporating direct AI interaction
How we can use Generative AI (GAI) to solve problems
Weeks 1-4How we can solve problems posed by GAI
Weeks 5-7Students will engage directly with article authors through guest presentations and participate in AI-assisted learning activities throughout both themes.
While ML is a component, data science requires broader interdisciplinary thinking
Inference (understanding "why") is equally important as prediction ("what")
Hardest parts are often data collection, cleaning, and conceptualization
Requires skills from humanities, social sciences, and philosophy
It's a collection of disciplines working together, like "science" itself
Data science creates an interconnected environment where multiple disciplines collaborate to solve complex problems
Linear process workflow:
Circular process emphasizing human agency at every step:
Key insight: Human judgment and agency drive the transformation at each stage, creating a dynamic cycle where data continuously evolves through human interpretation and action.
Complex preservation and curation system:
Understanding why certain data exists and how that affects conclusions
Addressing missing data and selection bias often more critical than sophisticated algorithms
All data involves human judgment in collection and conceptualization
Demonstrated how addressing missing data and selection bias through statistical imputation was more critical than sophisticated machine learning algorithms for predicting drug approval success.
AI should augment rather than replace human intelligence
AI as supportive, interesting, and safe environmental enhancement
Focus on creating useful tools rather than replicating human cognition
Engineering-focused on creating useful tools and applications
Research-focused on understanding intelligence itself
Class discussions showed diverse AI definitions ranging from "computer systems simulating human behavior" to "superhuman intelligence," highlighting the field's complexity.
AI should do good and benefit humanity
AI should not cause harm
Respect for human decision-making
Fair distribution of benefits and risks
AI systems must be interpretable and transparent
Unlike traditional technologies (medicine, engineering), AI faces unique demands for explanation, possibly due to lower societal trust in computer scientists and statisticians compared to medical professionals.
Learning data science is like acquiring new "languages" - each discipline has its own grammar, vocabulary, and way of thinking.
Ability to navigate technical, philosophical, ethical, and societal dimensions simultaneously.
This inaugural session established the course's ambitious scope: moving beyond technical data science skills to develop sophisticated, multidisciplinary thinking capabilities essential for navigating an AI-integrated future. The course emphasizes the intersection of technology, philosophy, ethics, and society in understanding both the potential and challenges of artificial intelligence.