Data-Portfolio


Project 1

LEGO Set Explorer - Interactive Power BI Dashboard for Product & Pricing Analysis 
Source: Maven Analytics Project

Overview

The LEGO Set Explorer is an interactive Power BI dashboard designed to analyze LEGO product data across themes, pricing tiers, and age segments.

The goal of this project was to simulate a real-world product analytics scenario by exploring how product complexity (pieces), brand licensing, and age targeting influence pricing and product distribution.

This dashboard demonstrates end-to-end analytics thinking — from data modeling to business insight delivery.

What I Built

  • Developed a fully interactive Power BI dashboard
  • Designed KPI cards to track:
    • Total Sets
    • Average Pieces
    • Average Price
  • Built dynamic filtering by:
    • Theme Group
    • Theme
    • Age Range
    • Category
    • Price
  • Implemented a Decomposition Tree to analyze product hierarchy and theme performance
  • Created a detailed set-level view showing:
    • Set Name
    • Set ID
    • Pieces
    • Retail Price
    • Price Tier
    • Dynamic Image Display
  • Designed clean UI layout for usability and executive presentation

Insights Delivered

  • Licensed themes (e.g., Star Wars, Marvel) contribute significantly to total product volume.
  • Product complexity (piece count) strongly correlates with retail pricing.
  • Younger age segments tend to have lower piece counts and mid-range pricing tiers.
  • Certain themes show higher pricing power independent of piece count, indicating brand value impact.
  • The dashboard enables quick identification of high-value and premium sets.

Tools 

  • Power BI Desktop
  • DAX
  • Data Modeling

Skills Demonstrated

  • Business Analysis
  • Product Analytics
  • KPI Development
  • Data Visualization & Storytelling
  • Hierarchical Analysis (Decomposition Tree)
  • Dashboard UX Design
  • Insight Communication

 


Project 2

Retail Performance KPI Dashboard (Power BI)
Source: Maven Analytics Project

Overview

An interactive Power BI dashboard designed to monitor retail performance through key KPIs, including total orders, revenue, and profit, with clear monthly trends.

What I Built

  • KPI cards highlighting current performance for orders, revenue, and profit
  • Monthly revenue trend analysis using a structured date table and hierarchy
  • Product category comparison to identify demand distribution and top performers

Insights Delivered

  • Clear month-over-month revenue trends and seasonality
  • Immediate visibility into top product categories by order volume
  • Executive-ready layout supporting fast, data-driven decisions

Tools & Skills

Power BI, DAX, Data Modeling, KPI Reporting, Time-Series Analysis

 

 


Project 3

Manufacturing Downtime Analysis Dashboard (Power BI)
Source: Real-world Operations Analytics Case (Daikibo)

Project Overview

An interactive Power BI dashboard analyzing manufacturing downtime across factories and device types. The project focuses on identifying operational bottlenecks, high-impact devices, and downtime distribution to support maintenance and process optimization decisions.

Key Objectives

  • Analyze total downtime by factory and device type
  • Identify critical machines contributing to the highest downtime
  • Support data-driven maintenance prioritization

Analysis & Approach

  • Modeled operational downtime data and created DAX measures to calculate total downtime minutes
  • Used bar charts to compare downtime across factories and device categories
  • Structured visuals to highlight high-impact devices and operational risk areas

Key Insights

  • Downtime is highly concentrated in specific factories and laser-based devices
  • A small number of device types account for the majority of downtime
  • Targeted maintenance efforts can significantly reduce operational disruptions

Tools & Skills

Power BI, DAX, Data Modeling, Operational Analytics, KPI Reporting

 

Project 4

Applied Data Analytics & Case Studie
Source: Google Data Analytics Capstone

Overview

This case study analyzes 24 months of bike-share trip data to uncover key behavioral differences between casual riders and annual members. The goal was to generate data-driven recommendations that help increase membership conversions and improve operational efficiency.

🧰 Tools & Technologies

  • BigQuery – Data ingestion and transformation

  • Python (pandas) – Cleaning and combining large CSV datasets

  • Power BI – Interactive dashboard development and storytelling

  • Excel – Initial data validation and trend checking

💡 Key Skills Demonstrated

  • Data Cleaning and Transformation

  • SQL Querying and BigQuery Modeling

  • Data Visualization and Dashboard Design

  • Business Insight and Strategic Recommendations

📊 Highlights

  • Annual riders make up 62% of all rides, reflecting strong loyalty and commute-based usage.

  • Casual riders take longer trips, often on weekends and in summer months.

  • Peak ride activity occurs around 8 AM and 5 PM, matching commuting hours.

  • Summer (June–August) shows the highest ridership for both user types.

  • The Power BI dashboard visualizes trends by duration, day of week, and user type for business insight.

🧰 Downloadable Report Link

📈 Dashboard Visuals

🧭 Key Insights

  • Members focus on short, frequent, weekday rides (commuting behavior).

  • Casual users prefer longer, weekend rides (leisure and tourism).

  • Seasonality impacts casual ridership more sharply than members.

  • Targeted promotions and seasonal campaigns can significantly increase membership conversion.

🧩 Recommendations

  1. Weekend-to-Membership Promotions – Offer discounts to casual riders with frequent weekend usage.

  2. Commuter Bundles – Position annual membership as a commuter convenience.

  3. Seasonal Campaigns – Market heavily before summer peaks.

  4. Behavioral Nudges – In-app reminders highlighting cost savings for frequent casual riders.

  5. Station-Level Targeting – Focus on high-casual-use locations (parks, waterfronts).

🏁 Conclusion

This project demonstrates my end-to-end data analytics workflow — from data extraction in BigQuery to visualization in Power BI — and translates analytics into actionable business recommendations.

It reflects my ability to connect data insights with real business impact, a key skill for any data analytics professional.

👤 About the Analyst

Belal Abu-Kadejah
Data Analytics Professional
Focused on delivering data-driven business insights through visualization, storytelling, and strategic thinking.
Tools: SQL | BigQuery | Python | Power BI | Excel