Skills
As a data science student, I am continuously developing my technical toolkit
for working with data, building models, and communicating analytical results.
Below is a summary of the tools and technologies I use most often, along with
languages and areas I am actively learning.
Programming Languages
- Python – Data analysis, visualization, and machine learning
- Java – Currently learning to strengthen programming fundamentals
- SQL – Basic querying and data retrieval
Data Analysis & Visualization
- Pandas & NumPy for data cleaning and manipulation
- Matplotlib & Seaborn for statistical visualization
- Plotly for interactive visualizations
- Exploratory Data Analysis (EDA) and feature exploration
Machine Learning
- Scikit-Learn for regression and classification models
- Model evaluation using appropriate performance metrics
- Feature engineering and data preprocessing
- Understanding of ensemble methods (e.g., Random Forest, Gradient Boosting)
Data & Research Skills
- Data storytelling and communicating insights clearly
- Statistical thinking and interpretation of results
- Working with real-world, messy datasets
- Reproducible analysis and structured workflows
Tools & Platforms
- Jupyter Notebook & VS Code
- Git & GitHub for version control
- Microsoft Excel for data handling