Stack
Available for Projects
Stack
Available for Projects
I work with a practical stack of tools that support every stage of the data science lifecycle — from data wrangling and modeling to visualization, deployment, and system design.
Core Programming & Data
Python
Serving as the primary engine for end-to-end machine learning, automation scripts, and scalable backend logic.
Python
Serving as the primary engine for end-to-end machine learning, automation scripts, and scalable backend logic.
R
Utilized for advanced statistical modeling, complex data visualization, and exploratory data analysis.
R
Utilized for advanced statistical modeling, complex data visualization, and exploratory data analysis.
SQL
Architecting complex relational queries and managing database schemas to extract high-value insights from datasets
SQL
Architecting complex relational queries and managing database schemas to extract high-value insights from datasets
Pandas
Using it for high-performance data manipulation, streamlining feature engineering and the preprocessing of structured data.
Pandas
Using it for high-performance data manipulation, streamlining feature engineering and the preprocessing of structured data.
NumPy
Executing efficient, vectorized mathematical operations and managing multi-dimensional array computations for low-latency processing.
NumPy
Executing efficient, vectorized mathematical operations and managing multi-dimensional array computations for low-latency processing.
Modeling & Statistical Tools
Scikit-learn
Implementing a wide array of supervised and unsupervised machine learning algorithms, focusing on model selection and robust cross-validation.
Scikit-learn
Implementing a wide array of supervised and unsupervised machine learning algorithms, focusing on model selection and robust cross-validation.
GLM
Applying Generalized Linear Models to solve regression and classification problems where traditional linear assumptions do not hold
GLM
Applying Generalized Linear Models to solve regression and classification problems where traditional linear assumptions do not hold
Deep Learning / ML Libraries
Tensorflow
Building and training deep neural networks, including CNNs and RNNs, with a focus on graph optimization and large-scale deployment.
Tensorflow
Building and training deep neural networks, including CNNs and RNNs, with a focus on graph optimization and large-scale deployment.
YOLO
Leveraging state-of-the-art "You Only Look Once" architectures for real-time object detection and high-speed computer vision tasks.
YOLO
Leveraging state-of-the-art "You Only Look Once" architectures for real-time object detection and high-speed computer vision tasks.
Visualization & Dashboards
Matplotlib
Generating static, publication-quality plots and figures to communicate underlying data distributions and model results.
Matplotlib
Generating static, publication-quality plots and figures to communicate underlying data distributions and model results.
Tableau
Designing interactive business intelligence dashboards that translate complex data metrics into accessible executive-level insights.
Tableau
Designing interactive business intelligence dashboards that translate complex data metrics into accessible executive-level insights.
Deployment & Infrastructure
Docker
Containerizing applications to ensure environment consistency across development, testing, and production phases of the lifecycle.
Docker
Containerizing applications to ensure environment consistency across development, testing, and production phases of the lifecycle.
AWS
Managing cloud-based compute and storage resources (EC2, S3, Lambda) to deploy scalable, highly available ML services.
AWS
Managing cloud-based compute and storage resources (EC2, S3, Lambda) to deploy scalable, highly available ML services.
