Computer science at Penn State, followed by graduate study at Maryland. The ideas behind the things I build.
M.S. Information Systems and Artificial Intelligence
Graduate coursework at the University of Maryland builds on a computer science foundation by adding business-facing depth in databases, Python and R analytics, data science, cloud computing, AI for business, digital transformation, project management, business process analysis, finance information systems, data visualization, causal inference, and industry practicum work.
Python, R, predictive analytics, AI for business, causal inference, and analytical decision-making.
Applied Python to process, analyze, model, and visualize business data, with emphasis on practical data manipulation and analytical workflows.
Used R to connect data, statistical models, and business decisions through analytical workflows and decision-oriented modeling.
Applied data science and predictive analytics methods to business problems, connecting statistical learning, model evaluation, and decision-making.
Explores how AI can be applied to business problems, including AI strategy, use-case design, governance, and practical adoption.
Studies causal reasoning and A/B testing methods used to evaluate interventions, platform changes, and business experiments.
Database management, enterprise cloud computing, big data, and modern data infrastructure.
Strengthened database foundations through relational design, SQL, data modeling, normalization, database architecture, and business-facing data management.
Covers enterprise cloud computing, big data systems, distributed data processing, and modern infrastructure used to support scalable analytics.
Digital transformation, digital markets, business models, and technology-enabled strategy.
Studied how organizations use digital technologies to transform business models, operations, customer experiences, and competitive strategy.
Explored digital markets, platforms, customer behavior, network effects, and strategy in technology-enabled business environments.
Business process analysis, project management, industry practicum, and implementation discipline.
Developed project planning and execution skills for dynamic business and technology environments, including scope, stakeholder coordination, risk, and delivery discipline.
Develops process modeling, systems analysis, requirements thinking, and business process improvement skills for information systems projects.
Capstone-style practicum focused on applying technology to solve a real-world business problem with an external company or organization.
Applied finance information systems, data visualization, dashboards, and communication of insights.
Connected finance concepts with information systems and analytics, supporting work in financial modeling, trading strategy analytics, risk metrics, and data-driven finance.
Develops data visualization techniques for exploring, communicating, and presenting data-driven business insights clearly and effectively.
B.S. Computer Science
Undergraduate coursework built a technical foundation across programming, algorithms, databases, operating systems, theory of computation, programming languages, numerical computing, probability, statistics, computer organization, and business fundamentals.
Programming, algorithms, systems, databases, theory, and software construction.
Introduced programming fundamentals, problem decomposition, control flow, functions, and computational thinking.
Expanded programming ability through intermediate software design, data structures, modular code, and more complex problem-solving.
Developed object-oriented programming skills and applied software design concepts in web-oriented programming contexts.
Studied logic, proofs, sets, relations, combinatorics, graphs, and mathematical reasoning used in algorithms and theoretical computer science.
Studied relational database design, SQL, data modeling, normalization, transactions, and database-backed application design with a writing-intensive component.
Studied programming language design concepts, abstraction, syntax, semantics, and how language choices affect software structure.
Studied formal models of computation, automata, computability, and complexity foundations behind what machines can solve.
Studied core data structures, algorithm design, runtime analysis, and efficient problem-solving under computational constraints.
Data mining, statistics, probability, numerical methods, and analytical modeling.
Covered exploratory data mining concepts used to discover patterns, structure, and predictive signals in data.
Studied computational methods for solving numerical problems, supporting modeling, optimization, simulations, and quantitative analytics.
Introduced numerical analysis methods for approximating and solving mathematical problems computationally.
Studied statistical inference, estimation, and mathematical statistics foundations used in model evaluation and analytics.
Operating systems, computer organization, digital design, and low-level systems thinking.
Built low-level programming and systems understanding, connecting software behavior to memory, processes, and machine-level execution.
Studied operating system concepts including processes, scheduling, memory, concurrency, file systems, and resource management.
Studied digital logic, circuits, and hardware-oriented design foundations that support understanding of computer architecture.
Studied how computer hardware and software interact, including instruction execution, memory hierarchy, and processor organization.
Calculus, linear algebra, probability, statistics, and vector analysis.
Built foundational calculus skills used in modeling change, optimization, and quantitative analysis.
Extended calculus foundations through integration techniques, sequences, series, and mathematical modeling.
Studied matrix operations and linear algebra concepts used in machine learning, optimization, statistics, and data transformations.
Studied multivariable calculus and vector analysis concepts relevant to mathematical modeling and computational problem-solving.
Studied probability foundations used in statistics, machine learning, risk modeling, and uncertainty-aware decision-making.
Business fundamentals connecting technical systems to markets, operations, finance, and organizations.
Studied accounting concepts used to interpret financial information and support managerial decision-making.
Studied finance fundamentals including valuation, risk, return, and financial decision-making.
Studied marketing principles, customer behavior, positioning, and market strategy.
Studied management, organizational behavior, and how teams and companies structure work.
Studied legal and regulatory concepts that shape business decision-making and organizational risk.
Studied microeconomic decision-making, markets, incentives, and resource allocation.
Studied supply chain concepts, operational flow, sourcing, logistics, and systems coordination across business networks.
Studied the intersection of technology, work, employment relations, and organizational systems.
Independent study coursework used to explore a focused academic or professional topic beyond the standard course structure.
Public speaking, rhetoric, technical writing, and professional communication.
Developed public speaking, presentation structure, audience awareness, and persuasive communication skills.
Developed foundational writing, argumentation, rhetoric, and composition skills.
Developed technical writing skills for documentation, reports, professional communication, and engineering-oriented writing.
Introductory seminar coursework supporting academic transition, discussion, writing, and university-level learning.
Security, risk analysis, human behavior, society, ethics, and broader context.
Introduced security, risk analysis, systems thinking, threat awareness, and decision-making under uncertainty.
Introduced human behavior, cognition, motivation, perception, and psychological foundations.
Studied major world religions and cultural systems, strengthening global awareness and cultural context.
Physical sciences, environmental systems, design, art, psychology, health, and cultural context.
Studied mechanics, motion, forces, energy, and physical systems.
Studied electricity, magnetism, circuits, and physical principles underlying electrical systems.
Studied energy systems, environmental impact, sustainability, and resource use.
Studied geospatial information, maps, spatial thinking, and how location-based data supports modern decision-making.
Studied visual culture, art history, and design interpretation, contributing to broader design and aesthetic awareness.
Studied film as an artistic and communication medium, supporting visual storytelling and narrative thinking.
Covered first aid, safety, CPR, and emergency-response fundamentals.
Focused on fitness, wellness, and health-oriented habits.