🌍 Gaza, Palestine 🇵🇸

• 🎓 Computer Science Graduate
• 📊 Data Analyst Intern at DAWNSTRIKER
• 👩‍🏫 Teaching Assistant — Data Structures & Algorithms & Design Patterns
• 🤖 Focused on Data Science, Machine Learning, and Time Series Analysis
• 💻 Backend engineering background with Laravel & Node.js

I enjoy working with data to discover insights, build predictive models, and develop practical data-driven solutions.

My experience includes data preprocessing, exploratory data analysis (EDA), feature engineering, data visualization, machine learning workflows, and time series forecasting using Python and SQL.

Through academic teaching and software engineering experience, I developed strong analytical thinking, problem-solving skills, and structured development practices that support my growth in Data Science and Machine Learning.

• 📊 Data Analysis and Machine Learning workflows
• 📈 Time Series Forecasting using ARIMA and SARIMA models
• 🤖 Machine Learning model development and evaluation
• 🧠 Exploring Deep Learning fundamentals with TensorFlow and Keras
• ⚙️ Building automated and structured data workflows

• Teaching core data structures and algorithmic problem solving using Java
• Explaining OOP principles and software design concepts
• Guiding students through coding exercises and implementation tasks
• Preparing structured labs and technical examples

• Data Analysis & Visualization
• Machine Learning
• Predictive Modeling
• Time Series Forecasting
• Feature Engineering
• Exploratory Data Analysis (EDA)
• Artificial Neural Networks

Time series forecasting project analyzing Google Trends data to predict future public interest in ChatGPT.

• Collected and analyzed weekly Google Trends data
• Performed stationarity testing using ADF
• Applied ACF/PACF analysis and differencing
• Built ARIMA, SARIMA, and Auto ARIMA models
• Evaluated forecasting performance using MAE, RMSE, and MAPE

🔗 https://github.com/Maryam-Skaik/chatgpt-google-trends-forecasting

Artificial Neural Network project for predicting income categories using demographic and employment data.

• Built ANN models using TensorFlow and Keras
• Applied preprocessing pipelines and feature scaling
• Used Dropout and EarlyStopping for regularization
• Tuned hyperparameters using KerasTuner

🔗 https://github.com/Maryam-Skaik/adult-income-prediction-ann

Humanitarian data automation and synchronization platform.

• Automated data collection and synchronization workflows
• Reduced manual data entry by ~80%
• Built for challenging connectivity environments

Laravel • PostgreSQL • n8n • Google Sheets API

🔗 https://github.com/Maryam-Skaik/GazaMadadFlow

REST APIs • MVC Architecture • Databases • Automation • Clean Architecture

Turning data, algorithms, and software into practical solutions.