Hi! I’m Somadina Nwokora. I’m a data analyst skilled in Excel, Power BI, SQL, and Python. I turn messy datasets into clean models, build intuitive visualizations and dashboards, and craft data-driven stories that drive business decisions. I enjoy solving messy problems, automating repetitive workflows, and presenting insights that non‑technical stakeholders can act on.
This space showcases my key projects, with hands-on work in data cleaning, analysis, SQL queries, modelling, visualisation, and insight detailing.
Below are my core data analytics projects, with a breakdown of analysis description, tools used, skills applied, project objectives, and measurable results.
1. Global Fuel Affordability Analysis (2020-2026)
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Brief Description:
This analysis compares fuel prices across 84 countries and seven regions, evaluating metrics such as income, subsidies, and tax rates to assess fuel affordability. -
Project Goals:
To assess how fuel prices, tax percentages, subsidy support, and income levels shape fuel affordability across different continental regions and countries from 2020 to 2026, and to highlight the regions and countries most affected by affordability pressure. -
Tools & Skills:
- Excel (Dataset validation)
- Python (Data cleaning, Preparation, and Table aggregation)
- Power BI (Analysis and Visualization).
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Results: From the analysis, fuel affordability is shaped by prices, taxes, subsidies, and income levels. The greatest strain falls on regions like Oceania and Europe, though higher incomes there cushion the hardship. African countries also bear significant pressure, especially given their large low‑income populations. The Middle East benefits most overall.
2. Google (GOOG) Stock Performance Analysis (2004-2022)
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Brief Description: This analysis covers GOOG’s price behaviour over 21 years (2004–2025), summarizing growth patterns, volatility, the win rate (percentage of days with favourable closes), and a crisis-period analysis demonstrating the stock’s durability.
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Project Goals: To explore GOOG’s growth, analyze its growth indices to assess both long-term strategic potential and short-term trading opportunities.
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Tools & Skills Used:
- Excel (Dataset validation)
- Python (Data cleaning, Analysis, and Matplotlib visualization).
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Results: GOOG grew from 3.85 in 2004 to 177.86 in 2025, representing a roughly 4,520% gain across 584 billion traded shares. It stayed resilient, closing above its opening price on 50.8% of 5,478 trading days, while daily volatility remained moderate at 1.95% despite the 2008 financial and 2022 post-COVID shocks.
3. Coca Cola (KO) Stock Performance Analysis (1962-2022)
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Brief Description:
This analysis of Coca-Cola stock (KO) examines the growth pattern, volatility, and win rate that drove the stock's successful value gains over 60 years (1962 to 2022). -
Project Goals:
To analyze KO’s historical market performance from 1962 to 2022 by examining trading volume, average closing price, trading consistency, and volatility patterns to understand its short-term and long-term value creation, liquidity, and stability. -
Tools & Skills Used:
- Excel (Dataset validation)
- Power Query Data Cleaning
- Power BI Data Analysis and Visualization.
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Results: KO traded about 140 billion shares over 60 years (~15,000 trading days), closing higher than it opened on 48% of those days, with an average daily volatility of 1.74%. Closing prices rose from 0.04 in 1962 to 60.86 in 2022. Overall, price and volume trends indicate sustained growth and broad global acceptance of KO’s beverage products.