External Course

Exploring Biodigital Convergence

DSA ADS Course - 2022

Artificial Intelligence, Human Brain, Biodigital Convergence, Human Augmentation

Discuss technical, societal, political, military, legal and ethical aspects of human augmentation and biodigital convergence.

Exploring Biodigital Convergence - February, 2020


Digital technologies and biological systems are beginning to combine and merge in ways that could be profoundly disruptive to our assumptions about society, the economy, and our bodies. We call this the biodigital convergence.

Performance of the SARS-CoV-2 RT-PCR test as a tool for detecting SARS-CoV-2 infection in the population

DSA ADS Course - 2022

Performance of the SARS-CoV-2 RT-PCR test as a tool for detecting SARS-CoV-2 infection in the population

COVID19, Public Policy, Health Policy, SARS-CoV-2, RT-PCR Test, Infectious Potential, Laboratory Quality Assurance, Dr. Kary Mullis, Cycle Threshold Values

The Cost-Benefit Fallacy: Why Cost-Benefit Analysis Is Broken and How to Fix It

DSA ADS Course - 2022

Cost-benefit Analysis, Cost-benefit Fallacy, Public Investment Planning, Forecasting, Resource Allocation, Behavioral Science

Discuss cost-benefit analysis, cost-benefit fallacy, public investment planning, forecasting, resource allocation, welfare economics, behavioral science and behavioral economics.

What if scenario estimates are highly inaccurate and biased? What are potential costs of scenario inaccuracies seriously distorting effective resource allocation?

Foundations of Data Science - Textbook

DSA ADS Course - 2021


Avrim Blum, John Hopcroft, Ravindran Kannan 

June, 2016 



Computer science as an academic discipline began in the 1960’s. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical computer science covered finite automata, regular expressions, context free languages, and computability. 


Introduction to Graphical Models and Bayesian Networks

DSA ADS Course - 2021

Graphical Models, Bayesian Networks, Applied Probability, Probabilistic Causation, Causal Inference

Applied data scientists require high-level understanding of probability theory and specialized techniques to design systems and solve problems in the real world.  Graphical model formalism provides a natural framework for the design of new data science systems.