VAN DAN NGUYEN

Virtual Data Analyst Assistant

Jul 2024 - Aug 2024 Upwork Miami, USA

• Entered and updated data into various databases and systems, enhancing data retrieval efficiency by 25%.
• Generated and compiled reports from data analysis, improving report accuracy and enhancing overall data reliability.
• Interpreted data and offered insights to aid in decision-making, resulting in an increase in operational efficiency and supporting strategic initiatives.
• Developed and maintained dashboards and data visualization tools, enhancing data accessibility and increasing data retrieval efficiency by 50%.
• Managed correspondence and addressed inquiries concerning data, achieving a 90% satisfaction rate among stakeholders.

Data Analyst Intern

Mar 2024 - Jun 2024 Rhyme Daegu, South Korea

• Collaborated with the marketing team to analyze customer behavior, leading to a targeted marketing campaign that achieved a 25% higher conversion rate.
• Crawled data from e-commerce websites using Python and BeautifulSoup, and deployed a process that identified potential products, resulting in a 30% increase in product sourcing efficiency.
• Utilized Data Visualization Tools such as Google Looker to create interactive dashboards and detailed reports, resulting in a 30% improvement in report clarity and stakeholder engagement.

Data Analyst Freelancer

Mar 2024 - Mar 2024 Upwork Alexandria, Virginia, USA

• Collaborated with cross-functional teams to identify key performance indicators (KPIs) and established a dashboard to track them, leading to a 25% improvement in operational efficiency.
• Created interactive data visualizations using Microsoft Power BI, leading to a reduction in time spent on generating reports for stakeholders.
• Compiled a comprehensive report after analyzing insights, resulting in an improvement in decision-making efficiency.

Global Business

Jan 2021 – Feb 2025 Hannam University

Courses: Business Management, Statistics, Data analytic with Excel

Sale Data Analysis

Aug 2024 - Sep 2024

• Leveraged GROUP BY and SUM functions to compute total sales across different time frames, such as daily, weekly, and monthly trends.
• The DATE_FORMAT function was crucial for grouping transactions by month and generating clear, time-based reports for management.
• Using AVG functions in conjunction with JOIN operations, I computed the average transaction values, providing insights into how sales vary across different store locations.
• Utilize Common Table Expressions (CTEs) to simplify complex queries into more manageable segments and provide clear updates on product statuses.

Employee Churn Analyze

Jun 2024 - Jun 2024

• Utilize BigQuery for querying and managing large datasets. Data sources include HR records, employee surveys, performance metrics.
• Use Google Colab for data analysis and modeling. Employ Python libraries such as Pandas, NumPy, and Scikit-learn for data manipulation, statistical analysis, and machine learning.
• Apply machine learning techniques (Random Forest Classifier) to build models that predict employee churn.
• Generate visual reports using libraries like Matplotlib and Seaborn to illustrate key findings and trends.