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I am a Data Analyst and Business Intelligence professional pursuing an M.S. in Computer Science at the University of North Texas. My work sits at the intersection of data, business, and storytelling. I use SQL and Python to find the signal, Power BI to make it visible, and business context to make it useful. |
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My operating principle: the best dashboard is not the one with the most charts. It is the one that makes the next decision obvious.
| NOW: BUILDING | NEXT: DEEPENING | TARGET: DELIVERING |
|---|---|---|
| Executive-ready Power BI dashboards | Advanced SQL and dimensional modeling | Trusted decision-support systems |
| End-to-end analytics case studies | Python automation and ETL design | Scalable analytics workflows |
| Clear KPI and business narratives | Modern analytics engineering | Measurable business outcomes |
| ASK BETTER QUESTIONS | BUILD TRUSTED DATA | MAKE INSIGHTS VISIBLE | DRIVE THE NEXT ACTION |
|---|---|---|---|
| Define the decision | Clean, validate, model | Design focused dashboards | Communicate recommendations |
| LAYER | TOOLS & PRACTICES |
|---|---|
| Query & Programming | SQL · Python |
| Business Intelligence | Power BI · Tableau · Microsoft Excel · DAX |
| Analysis | Pandas · NumPy · Jupyter · scikit-learn |
| Data Engineering | ETL Pipelines · Data Cleaning · Data Modeling · Data Validation |
| Databases | MySQL · PostgreSQL |
| Workflow | Git · GitHub · VS Code · Azure |
| Business Practice | KPI Design · Requirements Analysis · Data Storytelling · Recommendations |
- Frame the decision before selecting the metric.
- Validate the data before trusting the visualization.
- Model for clarity so business logic remains understandable.
- Design for scanning so stakeholders find the signal quickly.
- Explain the implication because an insight without action is unfinished.
- Automate repeatable work through reliable ETL and reporting workflows.
Open my analytics playbook
| PHASE | THE QUESTION I ASK |
|---|---|
| Discover | What business decision are we trying to improve? |
| Define | Which KPIs genuinely represent success? |
| Prepare | Is the data complete, consistent, and trustworthy? |
| Analyze | What patterns, drivers, and exceptions matter? |
| Visualize | What is the clearest way to communicate the signal? |
| Recommend | What action should follow, and how will we measure it? |
Data Analytics Professional Certificate |
IBM Cybersecurity Analyst Professional Certificate |
Tata GenAI Powered Data Analytics |
Deloitte Data Analytics Job Simulation |
Accenture Software Engineering Job Simulation |
What these credentials add to my toolkit
| CREDENTIAL | FOCUS |
|---|---|
| Google Data Analytics | End-to-end analysis, SQL, visualization, and case studies |
| IBM Cybersecurity Analyst | Security analytics, databases, incident response, and risk |
| Tata GenAI Powered Data Analytics | AI-assisted analysis and business recommendations |
| Deloitte Data Analytics | Dashboarding, analysis, and forensic technology |
| Accenture Software Engineering | Software delivery, architecture, and development practices |
flowchart LR
A["Google Data Analytics"] --> B["SQL"]
B --> C["Python"]
C --> D["Power BI"]
D --> E["Data Engineering"]
E --> F["Analytics Engineer"]
style A fill:#07111F,stroke:#22D3EE,color:#E2E8F0
style B fill:#07111F,stroke:#38BDF8,color:#E2E8F0
style C fill:#07111F,stroke:#60A5FA,color:#E2E8F0
style D fill:#07111F,stroke:#A78BFA,color:#E2E8F0
style E fill:#07111F,stroke:#C084FC,color:#E2E8F0
style F fill:#07111F,stroke:#34D399,color:#E2E8F0
