Project Portfolio

Uniqlo Expansion Plan: Power BI

 

  • Objective: Conducted a comprehensive market study to evaluate and refine a 2-year expansion strategy for Uniqlo across French regions.

  • Analytical Approach: Performed internal SWOT and Business Model Canvas analyses alongside external competitive mapping using Porter’s Five Forces.

  • Data Modeling: Leveraged multi-year datasets (2021–2023) to analyze regional sales performance, customer demographics, and competitor turnover/market share.

  • Deliverables: Engineered an interactive Power BI dashboard for real-time data visualization and a strategic report justifying store-opening recommendations based on forecasted growth.

 

Power Automate Workflows

 

  • Objective: Redesign and automate the end-to-end training management process to eliminate manual handling of participant communications, reduce errors, and ensure consistent tracking across sessions.

  • Operational Challenge: Training sessions relied on repetitive, manual email sending and file updates at multiple checkpoints, creating inefficiencies, high error risk, and limited scalability as participant volume increased.

  • Automation Design: Independently designed and implemented a set of Microsoft Power Automate workflows triggered by time-based events and session milestones. Workflows were connected to Google Sheets, Gmail, and Drive to dynamically populate templates using participant and session data.

  • Process Logic: Automated the full lifecycle including invitations, expectation surveys, assessments, feedback collection, and authenticated certificate generation with unique IDs stored at the participant level to ensure traceability and verification.

  • Impact: Reduced manual workload by approximately 60%, improved data accuracy, and established a scalable, repeatable system for future training programs.

 

Relational Database: Music Festival

 

  • Objective: Designed and implemented a robust relational database from scratch to manage the complex operations of a multi-day music festival. This was part of our classwork.

  • Schema Architecture: Engineered a structured data model featuring normalized tables for Artists, Genres, Customers, Performances, and multi-tiered Ticket Pricing.

  • Functional Automation: Developed custom SQL triggers to automate business logic, such as dynamic ticket price validation based on performance type and category.

  • Operational Testing: Applied advanced SQL queries to test database integrity, manage relational constraints (foreign keys), and retrieve meaningful insights from customer and sales transactions.

     

     

     

Cinematic Insights: Movie Rating Analysis in R

 

  • Objective: Conducted a comprehensive data analysis to identify how different movie genres influence critical and audience reception as measured by IMDb ratings and Metascores.

  • Data Wrangling: Cleaned a large-scale IMDb dataset using the Tidyverse library, handling missing values, extracting primary genres through string manipulation, and engineering new time-duration variables.

  • Exploratory Data Analysis (EDA): Performed deep-dive statistics to uncover trends in movie duration by genre and identified the top-performing categories based on historical performance.

  • Statistical Modeling: Executed a Linear Regression model to determine the statistical significance of genre as a predictor for IMDb ratings.

  • Advanced Visualization: Developed high-impact visual assets using ggplot2, including ordered bar charts and box plots to illustrate the distribution and variance of ratings across significant genres.