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Data Analyst with 1+ years in Data Cleaning & SQL
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Detail-oriented aspiring data analyst with hands-on project experience cleaning, querying, and analyzing real-world datasets in Excel, SQL, and Tableau and basic python. Comfortable taking a dataset from raw and messy through structured cleaning to clear, decision-ready findings, diagnosing missing data, validating assumptions, and documenting limitations rather than presenting numbers at face value. Certified in Data Analysis by Google and ALX. A Nutrition and Dietetics background brings strong attention to detail and comfort interpreting technical, numbers-heavy information for a non-technical audience.
Certificate · Data Analytics
August 1, 2026 – June 30, 2026
ALX Africa
Certificate · Data Analysis
August 1, 2025 – June 30, 2025
ALX Africa
Certificate · Professional Foundations
August 1, 2025 – June 30, 2025
Imo State University
B.Sc. · Nutrition and Dietetics
August 1, 2021 – June 30, 2024
Moms' Mart Enterprise
Data Records and Retail Assistant
February 1, 2022 – December 1, 2022
Nigeria
Gifted Young Stars Academy Primary School
Teacher & Administrative Assistant
January 1, 2020 – July 1, 2020
Nigeria
Diabetes Risk Factor Analysis
January 1, 2026 – January 1, 2026
Cleaned a 768-row clinical dataset (Pima Indians Diabetes dataset) used to identify factors associated with a diabetes diagnosis in female patients. Diagnosed missing data disguised as biologically impossible zero values (e.g. 0 mg/dL glucose, 0 BMI) across five columns; replaced low-impact gaps (under 5% missing, e.g. Glucose, Blood Pressure, BMI) with column averages using AVERAGEIF, and excluded two columns (Skin Thickness, Insulin) from analysis after determining they were 30% and 49% missing respectively, judging that imputing that much data would bias the findings. Reviewed every column against medical plausibility (e.g. validated that a zero BMI or zero blood pressure is impossible, while a zero pregnancy count is valid), and flagged but retained edge-case outliers (a BMI of 67.1 and a 47-year-old patient with 17 recorded pregnancies) rather than silently deleting them. Validated the dataset for duplicate records and incorrect data types, and confirmed the target Outcome column contained only valid values before analysis. Produced a written data-limitations summary alongside findings, documenting which columns were excluded and why framed around a defined stakeholder brief and four specific business questions on age, BMI, and glucose risk thresholds.
COVID-19 Patient Outcomes Analysis
January 1, 2026 – January 1, 2026
Loaded and cleaned a 566,000+ row patient-level COVID-19 dataset (Mexican government open data, via Kaggle) in PostgreSQL, working through the full pipeline from raw CSV import to analysis-ready table. Resolved CSV import errors caused by mixed-type ID fields and undeclared columns, and corrected date fields stored in non-standard formats using TO_DATE. Investigated an apparent set of 1,486 duplicate patient IDs; traced the issue to spreadsheet-software corruption of ID values (scientific notation) rather than true duplicates, and confirmed zero genuine duplicate records before proceeding, avoiding the deletion of real patient data. Standardized inconsistent missing-value codes (97, 98, 99) across 15 pre-existing condition columns into a single labelled category, and removed biologically invalid records (e.g. ages outside 0–110) representing under 1% of the dataset. Wrote SQL queries using CASE WHEN, GROUP BY, and conditional aggregation to calculate mortality and ICU admission rates by pre-existing condition, age group, and sex correcting for sample-size imbalance by comparing rates rather than raw counts. Found that diabetic patients had a 29.6% COVID-19 mortality rate versus 9.2% for non-diabetic patients, that mortality rose from 1.5% in patients under 18 to 38.4% in patients over 65, and that cardiovascular disease was the strongest single predictor of ICU admission among seven pre-existing conditions tested.
Google Data Analytics Certificate
Google / Coursera
January 1, 2026 – Present
Google Data Analytics Certificate
January 1, 2026 – Present
Data Analysis Certificate
ALX Africa
January 1, 2025 – Present
Professional Foundations Certificate
ALX Africa
January 1, 2025 – Present
Recognition as Team Lead, SMFest Abuja 2025
SMFest
January 1, 2025 – Present
ALX Data Analysis Certificate
ALX
January 1, 2024 – Present
ALX Professional Foundations Certificate
ALX
January 1, 2024 – Present
Certificate of Participation — Leo Stan Eke Foundation Entrepreneurship Boost Program
Leo Stan Eke Foundation
January 1, 2024 – Present
Cultural Fit Analysis
The candidate's project work in public health (Diabetes, COVID-19) shows an interest in impactful data analysis. Their volunteer experience, including a Team Lead role, suggests a proactive and collaborative attitude. The pursuit of multiple data analytics certifications (Google, ALX) indicates a strong drive for continuous learning and self-improvement. While the professional experience is not directly in data analysis, the transferable skills in data records and administrative support, combined with project diversity, suggest a good cultural fit for a role that values meticulousness and problem-solving.
Soft Skills & Operational Fit
The candidate demonstrates strong critical thinking, attention to detail, and problem-solving skills through their project work, particularly in diagnosing data anomalies. Their experience as a Team Lead and in volunteer roles suggests teamwork and organizational capabilities. The ability to document limitations and frame analysis for stakeholders indicates good communication and adaptability, which are valuable for operational fit.