Data Science & Addiction Research Fundamentals

Earners of this digital badge have demonstrated an understanding of the foundational knowledge, responsibilities, and practical skills essential for applying data science methodologies to addiction research. Participants are assessed on competencies spanning core domains: behavioral data science, foundational addiction research methods, behavioral genetics, social determinants and public policy, neuroscience of addiction, and probability concepts, distributions, and statistical inference. This badge is issued by the Rutgers Addiction Research Center (RARC).

The program is delivered in in-person and online formats across Rutgers campuses. Learners complete 12 learning modules that include 36 structured assignments. Assignments include exercises on foundational concepts and data types, hands-on coding tasks in Jupyter Notebooks using Python (encompassing data wrangling, data visualizations including neuroimaging data, and computation and analysis), and written data analysis and interpretation assignments.

Specific Competencies include the following:

1. Demonstrate the ability to collect, clean, and explore addiction-related data using Python libraries and coding best practices, create meaningful visualizations (including neuroimaging and geospatial data), and critically interpret results for research and policy applications.

2. Design and evaluate ethically sound studies of substance use, operationalize addiction constructs using standardized instruments, and contextualize findings within relevant social, environmental, and developmental frameworks.

3. Explain how genetic predispositions (e.g., twin study designs, GWAS, polygenic scores) interact with environmental factors to influence substance use trajectories, interpreting gene–environment interplay with nuance and compassion.

4. Analyze how social determinants and adverse childhood experiences influence substance use behaviors by utilizing standardized metrics and geospatial techniques, and critically assess their implications for public policy to inform effective interventions and reduce addiction risk.

5. Apply fundamental probability rules and key distributions (e.g., binomial, normal) to model substance use data, assessing assumptions and using simulations to explore potential outcomes and risk factors.

6. Identify and analyze core neural circuits (e.g., mesolimbic dopamine pathway, limbic-basal ganglia, cortico-basal ganglia) that underlie reward, craving, and impulse control, using neuroimaging findings to connect brain adaptations with addictive behaviors.

7. Perform hypothesis testing, construct confidence intervals, compare groups with t-tests and ANOVA, and interpret proportions and chi-square analyses—evaluating effect sizes, statistical power, and p-values in the context of robust addiction research.

For further details or inquiries, please contact Dr. Jesse Liss (Jesse.Liss@Rutgers.Edu), Program Coordinator for the Training in Research Undergraduate Experience through the Rutgers Addiction Research Center, and/or the Rutgers Addiction Research Center.