Data Analyst with hands-on experience in Python and SQL, specializing in data cleaning, exploratory data analysis, and visualization. Skilled in building predictive models using machine learning techniques to uncover insights and support data-driven decision-making. Experienced in working with structured datasets, identifying trends, and delivering actionable results through analytical problem-solving.
Data Analyst
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Contract
Summary
Performed customer transaction data analysis to evaluate transaction volume per customer, detect high-value transactions, measure transfer frequency, and identify anomalous spikes in activity using analytical techniques for pattern recognition and decision support.
Highlights
Queried and extracted structured data using SQL for analytical processing and reporting.
Analyzed 10,000+ customer transaction records using SQL to identify high-value transactions and behavioral patterns.
Detected anomalous transaction spikes using statistical techniques, improving fraud detection insights.
Performed frequency and trend analysis to uncover customer activity patterns and support financial decision making.
Delivered insights that highlighted key risk indicators and transaction behaviors.
Data Analyst
Port Harcourt, Rivers, Nigeria
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Contract
Summary
Executed a freelance project analyzing sales and business performance data, gaining experience in tracking revenue trends and understanding customer behavior. Delivered actionable insights to support business decision-making and operational planning.
Highlights
Cleaned, validated, and standardized real-world datasets using Python (Pandas).
Performed exploratory data analysis (EDA) to identify trends, patterns, and anomalies.
Analyzed sales and revenue data to track monthly growth and customer behavior.
Built dashboards to visualize KPIs, top-performing products, and revenue trends.
Automated reporting workflows, reducing manual analysis time by 30%.
Communicated insights clearly to support business and operational decisions.
Data Analyst Intern
Port Harcourt, Nigeria
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NYSC
Summary
Gained hands-on experience analyzing real-world datasets and applying data analysis techniques during an NYSC internship program.
Highlights
Worked with structured datasets (CSV, Excel, SQL tables) for analysis and reporting.
Applied data cleaning techniques: handling missing values, removing duplicates, and standardizing formats.
Created visual reports and dashboards using Tableau and Power BI.
Supported decision-making by summarizing insights in clear, non-technical language.
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Summary
Designed and deployed a machine learning pipeline for predicting customer churn behavior. Conducted exploratory data analysis to identify key drivers of churn such as age, balance, and product usage. Engineered categorical and numerical features using pipeline-based preprocessing. Trained and optimized classification models with hyperparameter tuning. Delivered a model with strong recall performance to reduce missed churn predictions.
Python, Scikit-learn
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Summary
Developed an end-to-end classification model to predict borrower loan default risk. Performed data cleaning, missing value treatment, and feature engineering to improve model performance. Applied one-hot encoding and feature scaling while preventing data leakage through proper train-test splitting. Trained and compared Logistic Regression, Random Forest, and Gradient Boosting models. Evaluated models using Accuracy, Precision, Recall, F1-score, ROC-AUC, and Confusion Matrix. Saved trained model for inference on unseen data
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B.Sc.
Computer Science
Communication, Problem solving, Team Collaboration, Adaptability.
Python.
Machine Learning, SQL Querying and Data Manipulation, Exploratory Data Analysis(EDA), Data Wrangling, Data Scraping, Time Series Forecasting, Model Evaluation & Validation.
Issued By
AltSchool Africa
Native