About Me
Hi! I'm Tien 👋I turn data into decisions.As a certified Data Analyst with a project management background, I create Power BI dashboards and Python analyses that help companies understand their story.I've worked across Germany, Singapore, and Vietnam – bringing international perspective to every dataset I touch.Let's make your data work for you.
Skills
Python | Power BI | Excel | SQL | Azure
Featured Projects
Power bi
MY CV IN POWER BI
SQL | Power bi
RFM Customer segmentation Dashboard
A Power BI dashboard using RFM analysis to segment customers and optimize marketing strategies for retention and growth.
Power bi
HR Dashboard
Enterprise HR Analytics Dashboard built with Power BI | Employee performance, turnover analysis, compensation insights, and workforce planning | DAX measures, star schema data model, interactive visualizations
Power bi
HR Overview - IBCS Style
A Power BI HR analytics report designed using IBCS principles. Includes multi-year workforce trends, headcount dynamics, hiring analysis, terminations, and organizational insights.
Excel
IT Helpdesk Analytics Dashboard
Comprehensive Help Desk analytics project using Excel Power Query, Power Pivot, and DAX to transform ticket data into actionable business intelligence.
Excel
Regional Revenue Dashboard
Regional revenue analytics dashboard for multi-location retail chain using Excel to drive strategic decision-making
Excel
Beverage Sales Dashboard
A Power BI dashboard using RFM analysis to segment customers and optimize marketing strategies for retention and growth.
Power BI
Sales Dashboard
Interactive Power BI dashboard analyzing $2B+ electronics sales across 4 US regions with advanced DAX measures, YoY comparisons, and regional performance insights
Power bi
HR Dashboard - Demographics and Training
Enterprise HR Analytics Dashboard built with Power BI | Employee performance, turnover analysis, compensation insights, and workforce planning | DAX measures, star schema data model, interactive visualizations
power bi
IT Service Ticket Overview
Multi-channel IT service ticket analysis dashboard built with Power BI. Analyzes customer support performance, SLA compliance, and resolution patterns across calls, chats, emails, and escalations.
SQL
Customer Retention & Revenue Optimization Analysis
Advanced SQL portfolio featuring 15+ business analytics queries: exploratory analysis, customer/product segmentation, RFM scoring, cohort retention tracking, YoY performance, and revenue attribution.
python
Tech stock dashboard
Interactive stock analysis dashboard built with Streamlit, featuring technical indicators, correlation heatmaps, and time-series analysis for major tech stocks
python
bitcoin price analysis dashboard
Interactive Bitcoin analytics dashboard with multi-timeframe analysis, volatility tracking, and investment insights. Built with Python, Streamlit, and Plotly.
python
Sales Analysis
Analysis and visualization of sales data including monthly trends, hourly patterns, product groupings, and city-wise performance using Python and Matplotlib/Seaborn
python
Youtube Sentiment Analysis
Data analysis project examining YouTube engagement patterns, comment sentiment, emoji usage, and channel metrics with interactive visualizations using Python, Pandas, and Plotly.
Professional Certifications
My growing list of proprietary, exam-based certifications
Microsoft Certified Power BI Data Analyst PL-300 | Issued 2025
IBCS® Certified Analyst | Issued 2025
PCAD™ – Certified Associate Data Analyst with Python | Issued 2025
PCAP™ – Certified Associate Python Programmer| Issued 2024
Certified Project Management Associate IPMA® Level D | Issued 2019
SQL | Power bi
RFM Customer segmentation Dashboard
Questions this analysis answered:
Which customer segments generate the most revenue and profit?
1.Why did customer growth increase while revenue decreased?
2. How can we identify at-risk customers before they churn?
3. What is the average customer lifetime value across different segments?I took the following steps to create my analysis:
1. Extracted customer transaction data from Azure SQL Database and transformed it using Power Query.
2. Calculated RFM scores using percentile-based DAX measures to segment customers dynamically.
3. Created calculated tables to map RFM scores to business segments (Champions, Big Spenders, At Risk, Lost, etc.).
4. Built statistical visualizations including box plots, scatter matrices, and cohort analysis charts.
5.Implemented custom SVG visuals for segment badges and IBCS variance charts for year-over-year comparisons.
**6. **Designed interactive slicers and drill-through functionality for detailed customer exploration.
7. Formatted the dashboard with consistent color themes and optimized performance for 18,000+ customer records.Here are my key takeaways:
1. Big Spenders contribute 39.5% of revenue but remain inactive for 234 days, representing a $2-3M opportunity.
2. 38.6% of customers are "Lost" after making only one purchase, indicating a critical retention problem.
3. Champions (10% of customers) drive 30% of profit with 2.3x higher purchase frequency than the average.
4. The business experienced a 46.9% customer growth but 14.7% revenue decline, proving not all customers deliver equal value.
5. RFM segmentation enables targeted marketing strategies with measurable ROI by focusing resources on high-value, high-retention segments.
6. Statistical analysis revealed extreme distributions where 60-70% of customers buy once, while a small elite group makes 10-28 purchases.
Power bi
HR Overview - IBCS Style
Questions this analysis answered:
1. How has our workforce size evolved from 2020 to 2025?
1. What are the hiring and termination trends year-over-year?
3. Which departments have the highest headcount and turnover?
4. How is our workforce distributed across age groups, education levels, and performance ratings?
5. What is the total compensation expense and how has it changed over time?
6. Are hiring and termination rates improving or declining?I took the following steps to create my analysis:
1. Imported employee data (DataTable.csv) containing hire dates, termination dates, departments, and demographics
2. Created DAX measures to calculate active headcount, new hires, and terminations with time intelligence
3. Built year-over-year comparison measures and percentage change calculations
4. Designed conditional formatting measures for visual indicators (red/green for negative/positive trends)
5. Developed interactive slicers for year selection and metric filtering
6. Created visualizations combining KPI cards, bar charts, and detailed demographic breakdownsHere are my key takeaways:
1. Company experienced explosive growth from 126 employees (2020) to 813 (2023), then stabilized
2. 2023 was a correction year with 115 terminations (+101.8%) and only 92 new hires (-65.5%)
3. IT and Sales departments consistently maintain the largest workforce
4. Hiring rate normalized from an unsustainable 102.4% (2020) to 11.3% (2023)
5. Total salary investment recovered to $75.09M after optimization efforts
6. Current termination rate of 14.1% suggests ongoing workforce optimization
Power bi
HR Dashboard
Questions this analysis answered:1. What is the current workforce composition and how has it changed over time?2. Which departments face the highest turnover risk?3. How does compensation correlate with performance and retention?4. What is the performance distribution across departments and positions?5. Where should we focus retention and development efforts?6. What is the average tenure and at what stage are we losing talent?7. How do our metrics compare to industry benchmarks?---I took the following steps to create my analysis:1. Extracted and cleaned employee data for 308 employees from HRIS system.2. Built star schema data model with fact and dimension tables.3. Created 70+ custom DAX measures for KPIs and analytics.4. Designed five dashboard pages with consistent formatting.5. Implemented conditional formatting and interactive visualizations.6. Benchmarked metrics against industry standards.7. Documented findings and strategic recommendations.---Here are my key takeaways:1. Turnover rate of 32.47% exceeds industry benchmark. Production department shows 40% attrition.2. 68% of employees are average performers. Only 12% are high performers.3. Production department (34% of workforce) is the highest risk area.4. IT/IS is the top performing department with 3.2 score and 80% retention.5. Average tenure of 3.56 years indicates mid-career departures.6. Inverted termination pattern suggests systematic hiring issues.7. Sales department has lowest performance at 2.9 score.8. Risk matrix identifies clear departmental priorities for intervention.9. Executive Office has 100% retention and strong stability.10. Integrated dashboard reveals interconnected HR patterns across all metrics.
Power bi
Electronics sales
Questions this analysis answered:1. Which region generates the highest profitability?2. How has performance changed year-over-year?3. Which products drive the most revenue?4. Are there seasonal sales patterns?5. Which markets offer the best opportunities?---I took the following steps to create my analysis:1. Extracted 70,080 sales records covering 2021-2023 from the database2. Designed star schema data model with proper relationships3. Developed 51 custom DAX measures for dynamic calculations4. Implemented YoY comparison logic using SAMEPERIODLASTYEAR5. Created conditional formatting with region-specific colors6. Built interactive visualizations with metric switching7. Validated calculations through Python analysis8. Documented all measures for maintenance and transfer---Here are my key takeaways:1. West region leads with 25.1% profit margin—7.1 points higher than Midwest (17.9%)2. Three-year growth stagnation at $2.09B annually signals urgent need for intervention3. Uniform 21.5% margins across all products suggest commoditization4. Top Western states (NV, OR, CA, WA) deliver 25%+ margins vs. 19.9% in Northeast5. Minimal seasonality (±6%) indicates missed promotional opportunities
Power bi
IT Service Ticket
Questions this analysis answered:1. How has ticket volume evolved across all channels from 2021 to 2023?2. Which support channels consistently meet SLA targets and which underperform?3. What are the most common issue categories driving support volume?4. How do resolution times vary across different ticket types?5. What trends indicate improvement or deterioration in service quality?---I took the following steps to create my analysis:1. Extracted and validated 548,746 ticket records from ServiceNow system.2. Built Power BI data model with year, channel, and category dimensions.3. Created DAX measures for volume, SLA compliance, and resolution time calculations.4. Designed interactive dashboards with year-over-year trend analysis.5. Analyzed three-year patterns to identify performance gaps and opportunities.---Here are my key takeaways:1. Escalations critically underperform with only 18-21% SLA compliance and 10+ day resolution.2. Chats deliver fastest resolution at 1.41-1.45 days but represent only 15-16% of volume.3. Emails achieve highest SLA compliance at 91% with stable 3.65-day average resolution.4. Escalation volume decreased 5.9% YoY in 2023, indicating improved first-contact resolution.5. Mobile App Issues appear in top 5 across all channels, signaling product quality concerns.6. Chat channel is underutilized despite superior performance, representing expansion opportunity.7. Five categories drive 80%+ of volume: Debit Card, Mobile App, Account Questions, Fraud Alerts, Account Management.