Skills
Skills are framed as technical abilities and applied experience: shipped SQL, reporting systems, models, dashboards, product prototypes, workflow architecture, and graduate-level analytics foundations.
Skill index
The Postgres SQL skill opens the full finance and workforce analytics warehouse case study published on GitHub.
Analytics engineering
Warehouse-style SQL for payroll, vendors, commissions, T&E, employee dimensions, QA checks, and executive reporting.
Decision systems
Raw-to-analytics schema design, dimensions, fact views, bridge tables, and business-ready reporting layers.
Executive reporting
Leadership-facing KPI surfaces for revenue, support, operations, budget, product trends, and review cycles.
Modeling workflows
Repeatable analysis scripts for data cleaning, churn modeling, baseline scoring, and business interpretation.
Finance operations
Budget workbooks, planning models, source-data reconciliation, and finance-friendly analytical outputs.
AI product analysis
AI-assisted reporting, prompt systems, natural-language analysis patterns, and grounded business workflows.
Systems orchestration
Agent routing, validation loops, internal assistants, and controlled task decomposition for business systems.
Trust layer
Source-to-fact checks, variance thresholds, pass/review statuses, and dashboard-readiness gates.
Advanced technical abilities
This is not a course list. It is the capability layer behind the work: mathematical modeling, statistical reasoning, machine learning, database systems, simulation, visual analytics, and applied delivery.
Model selection, supervised and unsupervised learning, regularization, kernel methods, ensemble models, bias-variance tradeoffs, and principled model evaluation.
Proven ability
Able to reason about why a model works, how it fails on future data, and how to compare alternatives using validation and statistical rigor.
Python-based analytics pipelines, numerical computing, data cleaning, feature preparation, vectorized operations, and implementation of repeatable analysis workflows.
Proven ability
Connects programming fluency with analytics execution: transforming raw data into model-ready structures and interpretable outputs.
Linear and generalized linear modeling, diagnostics, residual analysis, variable selection, endogeneity awareness, experimental design, and causal interpretation.
Proven ability
Can frame analytical claims carefully: assumptions, confounders, model diagnostics, coefficient interpretation, and decision limits.
Discrete-event simulation, random variables, input modeling, output analysis, variance reduction, stochastic processes, and simulation-based planning.
Proven ability
Useful for operations and product systems where uncertainty, queues, capacity, timing, and probabilistic outcomes shape decisions.
Relational data modeling, schema design, SQL, indexing, query processing, optimization, transactions, concurrency control, recovery, OLAP, and distributed data concepts.
Proven ability
Strengthens BudgetDB-style work: designing data systems that are queryable, reliable, auditable, and ready for analytics or AI layers.
Large-scale data processing, transformation, visual reasoning, dashboard design, network/graph thinking, and communication of complex analytical patterns.
Proven ability
Turns technical analysis into executive-readable surfaces: visual structure, interaction patterns, KPI framing, and stakeholder-ready interpretation.
Search, planning, probabilistic reasoning, intelligent agents, algorithmic decision-making, Python-based AI assignments, and classical AI foundations.
Proven ability
Supports practical AI product judgment: when to use agents, how to reason about state/action spaces, and how AI workflows make decisions.
Dimensionality reduction, PCA, autoencoders, Gaussian processes, tree-based methods, neural networks, and representation-focused predictive modeling.
Proven ability
Builds the technical foundation for anomaly detection, segmentation, feature compression, similarity analysis, and AI/ML product signals.
Scoping analytics problems, translating business needs into methods, managing applied analytics projects, presenting findings, and delivering usable insight.
Proven ability
Shows the operator layer: not just modeling, but turning analysis into decisions, communication, adoption, and measurable organizational value.