✓ Data analysis and visualization (Python: Pandas, Matplotlib, Seaborn; R)  
  ✓ SQL and relational database management  
  ✓ Microsoft Excel (PivotTables, PivotCharts, formulas, and functions)
  ✓ Dashboards and interactive reporting (Tableau and Excel)
  ✓ Exploratory data analysis and statistical modeling  
  ✓ Data cleaning, processing, and pipeline automation  

    Analyzed real-world medication safety incident data from the National Reporting and Learning System 
    (NRLS) in England and Wales (2016–2021) using Python, Pandas, SQL/SQLite, Seaborn, Matplotlib, 
    and Squarify. Conducted end-to-end analysis to evaluate medication error patterns, reported harm,
    guideline-related contributing factors, workflow vulnerabilities, and spatial–temporal variation.
    Generated actionable insights to support patient safety, quality improvement, and targeted risk 
    reduction across healthcare settings.  

  Analyzed 10 years of U.S. hospital diabetic patient data (1999–2008) using Excel pivot tables, charts, 
  and interactive dashboards. Conducted end-to-end analysis of readmission trends, patient demographics, 
  departmental performance, and clinical complexity drivers. Engineered readmission_flag metrics, visualized 
  risk patterns by specialty, age group, and number of diagnoses, and synthesized actionable insights to 
  support hospital decision-making, targeted interventions, and care coordination strategies.

  Analyzed a synthetic, transaction-level retail sales dataset spanning 2022–2024 using Python, Pandas, 
  SQL/SQLite, Seaborn, and Matplotlib. Conducted end-to-end EDA to evaluate revenue and profit KPIs, 
  product category performance, regional trends, discount impacts, and customer purchasing behavior, 
  generating actionable insights into sales drivers, operational efficiency, and business performance 
  across time and geography.

  Analyzed a synthetic financial transaction dataset using SQL/SQLite and Tableau. Conducted end-to-end 
  exploratory data analysis (EDA) to examine transaction amounts, frequency patterns, and login behavior. 
  Engineered multi-factor fraud metrics to flag high-risk transactions, scored accounts by combined 
  anomalies, and visualized behavioral and geographic patterns to generate actionable insights for 
  proactive fraud detection and transaction monitoring.  

  Explored K–5 student reading log data from the March Madness Reading Challenge using Python, Pandas, 
  Seaborn, and Matplotlib.pyplot. Conducted EDA to track weekly reading trends, identify top performers 
  and at-risk students, and analyze class-level distributions and outliers to generate actionable insights.

  Conducted community-level analysis of Chicago census, public school, and crime datasets using Python, Pandas, 
  Seaborn, Matplotlib, and SQLite3. Performed EDA to explore distributions, temporal and spatial trends, and 
  correlations across socioeconomic, crime, and educational metrics, generating actionable insights for 
  policymakers and educators.

   Explored US COVID-19 confirmed case data from March through September 2020 using Python, Pandas, 
   and Matplotlib. Conducted EDA to track state-level trends, calculate numerical and percentage increases, 
   compute weekly average case growth, and analyze proportions of national cases, generating actionable 
   insights into pandemic dynamics across states and territories. 

 Graduate-level bioinformatics and phylogenetics research projects involving Bayesian and maximum likelihood 
 inference, coalescent species tree estimation, ancestral state reconstruction, molecular clock dating, gene 
 flow testing, and genetic distance modeling using R, R Markdown, RAxML, MrBayes, ASTRAL, phytools, phangorn, 
 MUSCLE, Clustal-Omega, and HPC-based workflows.

 Investigated the genomic basis of local adaptation and potential range expansion in Culex tarsalis, a primary 
 vector of West Nile virus, using reference genome analysis, orthogroup identification, gene duplication events, 
 protein function annotation, and population-level sequencing data. Conducted quality control and alignment of 
 884 Illumina paired-end samples, evaluated mapping efficiency, and explored alternative alignment strategies. 
 Performed comparative genomics to identify species-specific gene duplications, linked orthogroups to Gene 
 Ontology terms, and visualized functional patterns through frequency tables and word clouds, generating 
 actionable insights into environmental and genetic factors driving divergence among Pacific, Sonoran, and 
 Midwest mosquito populations to inform disease management and anticipate geographic spread.

  Analyzed war asset distribution across the Milky Way in Mass Effect 3 using Python, Pandas, Seaborn, 
  Matplotlib, and SQLite. Hand-curated dataset from a 100+ hour playthrough. Conducted exploratory data 
  analysis to quantify assets at region, cluster, and star system levels, modeled optimal visitation 
  sequences, visualized galaxy hierarchy with radial and cluster-focused plots, and generated actionable 
  insights for strategic resource collection and efficient gameplay.

  ✓ Programming [R, Python, SQL]  
  ✓ Scripting [Bash, UNIX/Linux Shell]  
  ✓ Statistical Analysis [Pandas, NumPy, Seaborn, NumPy, Matplotlib, TopGO, etc.]  
  ✓ Database Management & Modeling  
  ✓ Data Processing  
  ✓ Algorithm Development  
  ✓ Sequence Alignment  
  ✓ Molecular Data Analysis  
  ✓ Phylogenetics  
  ✓ Machine Learning

  ✓ Data Analysis Within a Database [SQL & Python]  
  ✓ Developing Basic to Intermediate SQL Queries for Data Manipulation Using DML Commands  
  ✓ Building and Structuring Relational Databases, Creating and Modifying Tables via DDL  
  ✓ Construct Complex Queries Leveraging Advanced SQL Features Including Views, Transactions, Stored Procedures, and Joins

  ✓ Foundational and Advanced Statistical Methods [Probability Models, Inference Techniques, Regression Analysis, and Experimental Design]  
  ✓ Programming [R, Python, SQL]  
  ✓ Scripting [Bash, UNIX/Linux Shell]