Hello, I'm Elias, an Information Technology Specialist with a strong focus on AI, Machine Learning, and Data Engineering. With a passion for solving complex problems and building innovative solutions, I have worked across various domains to leverage cutting-edge technologies and drive meaningful impact.

This website serves as my portfolio, where I share my work, projects, and accomplishments in the fields of AI, ML, Data science, and IT. Whether it's developing data-driven systems, designing intelligent algorithms, or building scalable infrastructure, I’m constantly seeking new challenges and opportunities to grow.

Thank you for visiting my site, and I hope you find my projects and experiences insightful!

eliasgirma.h@gmail.com

Education:

St. Mary's University 2021

Masters Degree in Computer Science

ALX Cloud Computing 2024

The Cloud Computing program provides practical skills in cloud technologies and infrastructure. Key topics include:

Cloud Basics: Overview of cloud models (IaaS, PaaS, SaaS), deployment types (public, private, hybrid).
Cloud Services: Hands-on with AWS Cloud for resource management and automation.
Cloud Security: Best practices for securing cloud environments and ensuring compliance.
Storage & Databases: Managing cloud storage solutions and databases (e.g., AWS S3).
Networking: Configuring cloud networks, including VPNs and load balancers.
DevOps & CI/CD: Automating deployments and managing cloud infrastructure with DevOps tools.
Cost Management: Optimizing cloud resources to manage and reduce costs.

10 Academy AI Engineering /Machine Learning Engineering/Data Engineering Program Summary

The 10 Academy program focuses on equipping learners with essential skills for careers in machine learning and data engineering. Key topics include:

Machine Learning Fundamentals: Introduction to supervised and unsupervised learning, classification, regression, and clustering algorithms.
Data Engineering: Working with large datasets, building ETL pipelines, and optimizing data flow for machine learning applications.
Advanced Machine Learning: Deep learning, neural networks, and model evaluation techniques using frameworks like TensorFlow.
Dashboard Preparation: Creating interactive dashboards to visualize data insights and machine learning results using tools like Dash.
Data Processing and Management: Handling structured and unstructured data.
Model Deployment: Deploying machine learning models in production environments using containerization and cloud platforms.
Real-World Applications: Practical projects covering topics such as predictive analytics, recommendation systems, and natural language processing (NLP).

The program culminates in a hands-on capstone project to apply machine learning and data engineering skills to real-world problems.

Work Experience

Information Security Officer January 10, 2018 - October 11, 2019

At Information Network Security Agency(INSA), Addis Ababa (Ethiopia)
- Develop and implement information security policies and procedures.
- Manage security incident response and investigations.
- Conduct security audits and assessments.

Monitoring and Evaluation (M&E) Officer October 12, 2019 - March 09, 2021

At CDC Project in Addis Ababa City Administration Health Bureau, Addis Ababa (Ethiopia)

Monitoring and evaluation coordinator supervision of the Health Facility Head works closely with the city Health Office. Mentor all HIV program staff on recording, analysis, and use.

IT Specialist March 09, 2021 - up to now

At CDC Project in Addis Ababa City Administration Health Bureau, Addis Ababa (Ethiopia)

Responsible for advising, maintaining, coordinating information flow, Managing CDR Data warehouse, updating, supporting as well as troubleshooting the health information system tools used in the health bureau in support of the HIV/AIDS programs like DHIS2, ART smart care, EMR, Full EMR, and various aspects of the office’s information technology needs.
Manage and support CDR system and other healthcare systems
Ensure data privacy and security
Implement and maintain interoperability standards

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Projects

Project Name: Ethi_Medical_DataWarehouse

Description:

Ethi_Medical_DataWarehouse is a comprehensive data management system aimed at centralizing and managing health-related data from medical institutions across Ethiopia. The project focuses on integrating diverse datasets to provide healthcare professionals, researchers, and policymakers with reliable insights for better decision-making.

Objectives:

Data Integration: Aggregate data from various hospitals, clinics, and healthcare facilities.

Data Quality: Improve data accuracy, consistency, and completeness for reliable analytics.

Data Security: Ensure compliance with privacy regulations, securing sensitive patient data.

Data Analysis: Enable advanced analytics, including predictive modeling, to assist in healthcare decision-making.

Reporting and Dashboards: Develop interactive dashboards for easy data access and visualization.

Key Features:

Centralized Data Storage: Use of scalable cloud-based storage solutions to house medical data.

Data ETL Pipeline: Efficient extraction, transformation, and loading (ETL) of data from multiple sources.

Data Warehousing Architecture: Design of a robust data warehousing model using modern technologies like SQL, NoSQL, and Big Data tools.

Technologies Used:

Backend: PostgreSQL

ETL Tools: Apache Kafka, Talend, or custom Python-based scripts

Data Visualization: Dash dashboards

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Project Name: Rossmann Sales Prediction

Description:

The Rossmann Sales Prediction project aims to forecast the daily sales of Rossmann drug stores across Germany. Using historical sales data, store attributes, and external factors such as promotions and holidays, the project utilizes machine learning techniques to predict sales trends and optimize inventory and staffing decisions.

Objectives:

Sales Forecasting: Predict daily sales for each store to assist with inventory and staffing planning.

Improved Business Decisions: Enable more accurate predictions for marketing campaigns, product stocking, and demand forecasting.

Data-Driven Insights: Leverage machine learning models to uncover patterns and trends in historical sales data.

Key Features:

Data Preprocessing: Clean and transform raw data into a format suitable for training machine learning models.

Feature Engineering: Create new features like store promotion trends, holiday impact, and weather patterns to improve model accuracy.

Model Training: Train multiple machine learning models (e.g., Random Forest, XGBoost, LSTM) to predict sales.

Evaluation: Evaluate model performance using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

Deployment: Develop an API to serve the trained model and provide real-time sales predictions.

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For more information on hiring from 10 Academy, contact team@10academy.org