AYODAMOPE ADESIYAN

Ayodamope-vitalytics.github.io

Welcome,I’m a data analyst with a background in nursing, passionate about turning health and human-centered data into actionable insights.

##About Me I specialize in data cleaning, visualization, and reporting using Excel, SQL, and Power Bi## Projects

📊 Project 1: Lifestyle and health prediction analysis

Description:- Analyzed real-world datasets across variables like employment, marital status, education, alcohol use, diet, and chronic illnesses. Tools Used: Excel, Power BI Key Findings:

Project Dashboard Preview

![Dashboard Lifestyle and Health prediction Analysis ]Screenshot (24))(Screenshot (25))(Screenshot (26))

📈 Project 2: Fashion Product Sales Trend Analysis

Description: - Cleaned and structured sales data from a fashion brand to identify best-selling product categories and seasonal performance

Tools Used: Excel, Power BI

Key Findings:

Project Dashboard Preview

Dashboard Fashion Products Data Analysis

📊 Project 3: APPLICANTS FOR DAWB DATA ANALYTICS TRAINING-COHORT 2

Description:-It presents an analysis of 100 applicants from the DAWB Data Analytics Training (Cohort 2), highlighting key insights from application sources, employment status, selection process, and geographic reach.

Tools Used: Excel, Key Findings:

-📊 Application Process: Out of 100 applicants, only 23 advanced to selection, showing DAWB’s strong emphasis on quality and readiness.

-🧭 Application Source: Referrals brought in the highest number of applicants, followed by social media. The DAWB community believes in and promotes the program — that’s true impact!

-💻 Employment Status: Most applicants are employed professionals, reflecting a growing interest in using data skills to enhance career growth.

-🌍 Geographic Spread: Lagos and the Southwest region dominate participation, showing DAWB’s strong regional influence and room to reach other zones.

View Project Files →)

Project Dashboard Preview

Dashboard APPLICANTS FOR DAWB DATA ANALYTICS TRAINING-COHORT 2 )

📊 Project 4:Mental health workplace survery

Description:-This project analyzes workplace burnout using a multi-dimensional dataset covering work hours, sleep duration, physical activity, years of experience, gender, and productivity metrics. I designed a comprehensive dashboard visualization to communicate patterns and risk factors contributing to employee burnout.

Tools Used: Excel Key Findings:

-Those working between 40–49 hours per week reported the highest burnout levels. It wasn’t the people working the longest hours — it was those in the “almost too busy” range. Sometimes, burnout doesn’t happen at the extreme; it creeps in when you think you’re still managing fine.

-There was only a small difference between male and female employees, but men showed slightly higher burnout levels. It’s a reminder that burnout has no respect for gender — it affects anyone, no matter how strong or composed they appear.

-People who get more sleep have higher productivity — rest isn’t a luxury; it’s an efficiency tool.

-Employees with the longest years of service had the highest burnout levels — loyalty shouldn’t lead to depletion.

-Those who engage in more physical activity report lower stress — small steps literally make a big difference.

-The more burnout increases, the less satisfied people feel with their jobs — when well-being declines, so does engagement. View Project Files →)

Project Dashboard Preview

Dashboard Mental health workplace survey )

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