Victor Adedero

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Data Analyst with 3+ years of experience transforming data into actionable insights. Skilled in Microsoft Excel for automation and reporting, SQL for data extraction and analysis, and Power BI for building interactive dashboards.

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Data Analysis Portfolio

PROJECT 1

📊 Customer Churn Analysis Dashboard

🔍 Project Overview

Customer churn is a major challenge for businesses, as losing customers directly impacts revenue and growth.
This project analyses customer data to identify churn patterns, understand customer behaviour, and highlight key risk factors.

🛠️ Tools Used

📈 Key Metrics

💡 Key Insights

📊 Dashboard Features

📁 Dataset

The dataset used represents e-commerce customer behaviour, including demographics, engagement, and churn indicators.

🖼️ Dashboard Preview

image

🚀 Conclusion

This dashboard provides a clear overview of customer churn patterns and highlights areas where businesses can focus their retention strategies to improve long-term profitability.

PROJECT 2

📊 Sales & Profit Performance Dashboard

🔍 Project Overview

This project analyses retail sales data to evaluate business performance across regions, product categories, customers, and shipping methods.

The goal is to identify:

The dashboard provides an interactive way to explore trends and uncover insights that support data-driven decision-making.

🛠️ Tools Used

📈 Key Metrics

💡 Key Insights

1. Profitability is Uneven Across Categories

👉 This suggests cost inefficiencies or heavy discounting in Furniture.

2. Sales Are Concentrated in Key States

👉 The business is geographically dependent on a few strong markets, increasing risk.

3. High Sales ≠ High Profit

👉 Indicates:

4. Customer Revenue is Highly Skewed

👉 Opportunity:

5. Shipping Mode Impacts Order Volume

👉 Suggests:

📊 Dashboard Features

Interactive Filters:

📁 Dataset

The dataset used is the Superstore Sales dataset, containing:

🖼️ Dashboard Preview

images

🚀 Business Recommendations

1. Improve Furniture Profitability

2. Reduce Over-Reliance on Key Regions

3. Optimise Discount Strategy

4. Focus on High-Value Customers

5. Leverage Cost-Efficient Shipping

🧠 Skills Demonstrated

PROJECT 3

SQL-PROJECT

TITLE: Workplace Safety Incident Analysis using SQL

SQL CODESsales data

Skill Used:Data Retrieval (SELECT): Queried and extracted specific information from the database. Data Aggregation (SUM, COUNT): Calculated totals, such as sales and quantities, and counted records to analyze data trends. Data Filtering (WHERE, BETWEEN, IN, AND): Applied filters to select relevant data, including filtering by ranges and lists.

Business Context

A manufacturing company is experiencing increasing workplace incident costs across multiple plant locations.

The operations team has requested a data analysis to:

Dataset

Workplace Safety Data

Key fields include:

SQL Questions and SQL Solutions

1. Identify all incidents that occurred in the Georgia plant

SELECT *
FROM [dbo].['Workplace Safety Data$']
WHERE PLANT='GEORGIA'

🖼️Preview

s1 —

SELECT *
FROM [dbo].['Workplace Safety Data$']
WHERE [INCIDENT TYPE] <> 'fall'

🖼️Preview

s2

3. Analyze incidents in key operational locations (California and Florida)

SELECT *
FROM[dbo].['Workplace Safety Data$']
WHERE PLANT IN ('CALIFORNIA','FLORIDA')

🖼️Preview

s3

4. Identify high-cost incidents in California (cost greater than 1000)

SELECT *
FROM[dbo].['Workplace Safety Data$']
WHERE PLANT = 'CALIFORNIA' AND [INCIDENT COST]>1000

🖼️Preview

s4

5. Identify incidents based on either location(CALIFORNIA) or cost condition(>1000)

SELECT *
FROM[dbo].['Workplace Safety Data$']
WHERE PLANT = 'CALIFORNIA' OR [INCIDENT COST]>1000

🖼️Preview

s5 —

6. Calculate average, total, and count of incident costs by plant and gender in the following plants (ALABAMA CALIFORNIA GEORGIA)

select [Plant]
  ,[Gender]
  ,avg([Incident Cost]) as avg_incident_cost
  ,sum([Incident Cost]) as total_incident_cost
  ,count(*) as number_of_incident
  from[dbo].['Workplace Safety Data$']
  group by [Plant]
          ,[Gender]
  having plant in ('alabama','california','georgia')
 order by[Plant] asc

🖼️Preview

26 —

7. Identify the highest-cost incident in each plant

with incident_rank as
(
select * 
       ,rank () over( partition by[Plant] order by [Incident Cost] desc) as rank
from[dbo].['Workplace Safety Data$']
)

select*
from incident_rank
where rank=1

🖼️Preview

s7 —

Key Insights

Tools Used

Conclusion

This project demonstrates the use of SQL to analyze workplace safety data and extract meaningful business insights. By applying filtering, aggregation, window functions, and conditional logic, key patterns in incident occurrence and cost distribution were identified.

The analysis highlights that incident costs vary significantly across plants and demographic groups, with certain locations consistently showing higher risk levels. High-cost incidents were successfully isolated and ranked, providing visibility into the most critical safety concerns.

Overall, this project shows how SQL can be used not just for querying data, but for supporting data-driven decision-making. The insights generated can help organizations prioritize safety improvements, reduce incident costs, and enhance operational efficiency.

PROJECT 4

POWER BI-PROJECT

📊 Superstore Sales Performance Dashboard Power BI

🔍 Project Overview

This project analyzes retail sales performance using Power BI, focusing on revenue, order volume, profitability, and shipping trends across different customer segments and regions.

The dashboard provides an interactive view of business performance between 2022 and 2023, enabling stakeholders to identify key trends and make data-driven decisions.

🎯 Objectives

📌 Key Metrics

📈 Dashboard Features

🧠 Key Insights

🛠 Tools Used

📷 Dashboard Overview

over

🚀 Business Value

This dashboard enables stakeholders to:

📬 Contact victor36629@gmail.com

If you’d like to collaborate or discuss this project, feel free to connect.