Passionate about transforming data into actionable insights through analytics, visualization, and machine learning solutions.
I am a Data Analyst with a background in Biology Education and over 12 years of experience turning complexity into clarity. Today, I apply that same problem-solving mindset to data—transforming raw information into meaningful insights that support informed decision-making.
I specialize in data cleaning, analysis, visualization, dashboard development, business reporting, and machine learning, using Excel, Power BI, SQL, and Python to build practical, end-to-end solutions.
My portfolio includes projects ranging from interactive business dashboards to recommendation systems, reflecting my growing expertise in analytics and machine learning.
An interactive HR analytics solution developed in Microsoft Excel to analyze workforce trends, employee turnover, recruitment effectiveness, engagement, and compensation.
Highlights
- Data cleaning using Power Query
- Interactive KPI dashboard
- PivotTables and PivotCharts
- Dynamic slicers
- Workforce and HR insights
- Business recommendations
🔗 Repository:
https://github.com/ijeoma-data/HR-Analytics-Dashboard-Excel
An interactive retail analytics project developed in Power BI to explore customer purchasing behavior, sales patterns, product performance, and shopping trends using the Istanbul Shopping Dataset.
Highlights
- Data cleaning and transformation using Power Query
- Data modeling in Power BI
- Interactive dashboard development
- DAX-based calculations
- Customer and sales analysis
- Business insights and visual storytelling
🔗 Repository:
https://github.com/ijeoma-data/Istanbul-Shopping-Analytics
A healthcare analytics project analyzing patient-level hypertension data to investigate factors associated with blood pressure treatment outcomes.
The project uses Excel to transform clinical data into an interactive analytical dashboard and generate insights that can support healthcare decision-making.
Highlights
- Data cleaning and preparation
- PivotTables and PivotCharts
- Interactive dashboard
- KPI development
- Analysis of treatment outcomes
- Slicer-driven exploration
- Healthcare insights and recommendations
🔗 Repository:
https://github.com/ijeoma-data/Hypertension-Control-Treatment-Effectiveness-Analysis
An end-to-end machine learning recommendation system developed using Collaborative Filtering (SVD) on the MovieLens Latest Small Dataset to generate personalized movie recommendations through an interactive Streamlit web application.
Highlights
- Collaborative Filtering using SVD
- MovieLens dataset analysis
- User-item interaction modeling
- Model evaluation using RMSE and MAE
- Personalized movie recommendations
- Streamlit web application
- Modular Python project structure
- Git and GitHub version control
- Live cloud deployment
🔗 Repository:
https://github.com/ijeoma-data/Movie-Recommendation-System
- Microsoft Excel
- Power BI
- SQL
- Python
- Streamlit
- Power Query
- Pandas
- NumPy
- PivotCharts
- Matplotlib
- Collaborative Filtering (SVD)
- Recommendation Systems
- Scikit-Surprise
- Model Evaluation (RMSE & MAE)
- Model Persistence (Joblib)
- Git & GitHub
- Certified Data Analyst – The Data Immersed (TDI)
- Certified Data Analyst – Nigeria 3MTT
- Teacher's Registration Council Of Nigerian (TRCN)
- Qualified Teacher Status (QTS) – Teaching Regulation Agency (UK)
- TEFL Certified
- Business Intelligence
- HR Analytics
- Retail Analytics
- Data Visualization
- Machine Learning
- Predictive Analytics
To leverage data analytics, business intelligence, and machine learning to develop data-driven solutions that improve decision-making and solve real-world business problems.
I am passionate about continuous learning and enjoy building end-to-end analytical and machine learning projects—from data preparation and visualization to model development and deployment.
- GitHub: https://github.com/ijeoma-data
- LinkedIn: https://www.linkedin.com/in/ijeoma-okeke-53123829b
- Portfolio App: https://movie-recommendation-system-ijeoma.streamlit.app/
- Email: ijeomaokekebiz@gmail.com