Conda environment, JupyterLab, NumPy, Pandas, Scikit-learn, and reproducible scientific workflows.
PostgreSQL, SQLite, DBeaver configuration, and Python SQLAlchemy connector initialization.
Power BI installation, data model connector setup, DAX query studio, and report template configuration.
PyTorch, CUDA acceleration, Google Colab Pro, and TensorBoard visualization setup.
Hugging Face Transformers, Datasets, Tokenizers, and SpaCy pipeline configuration.
OpenCV, Torchvision, Albumentations, and YOLOv8 real-time inference toolchain.
OpenAI, Anthropic, ChromaDB, Pinecone, LangChain, and embedding pipelines.
FastAPI, Docker, MLflow tracking server, and GitHub Actions CI/CD setup.
Linear algebra, calculus, probability distributions, hypothesis testing, and loss optimization functions.
Plotly, Seaborn, interactive visual storytelling, KPIs, and decision-maker presentations.
Missing data imputation, anomaly detection, schema validation, and feature transformation pipelines.
Univariate/multivariate analysis, correlation heatmaps, feature distributions, and business insight summaries.
Complex joins, window functions, CTEs, cohort analysis, and customer lifetime value computation.
A* pathfinding, minimax with alpha-beta pruning, constraint satisfaction problems, and rule engines.
Logistic regression, Random Forests, XGBoost, LightGBM benchmarking, ROC-AUC, and cross-validation.
K-Means, DBSCAN, PCA, t-SNE, customer segmentation, and dimensionality reduction strategies.
SHAP values, LIME explainers, feature attribution, fairness auditing, and executive interpretability.
ARIMA, Prophet, LSTM networks, trend seasonality decomposition, and demand forecasting.
DAX measures, star schema modeling, interactive drill-throughs, and executive metric summaries.
PySpark distributed DataFrames, map-reduce architectures, and scalable inference services.
Comprehensive data science portfolio project combining ETL, ML modeling, and business ROI analysis.
Object-oriented machine learning framework, custom Scikit-learn transformers, and testing suites.
PyTorch custom neural network architectures, backpropagation, residual networks, and learning rate schedulers.
Fine-tuning BERT, sentiment classification, NER extraction, and text generation pipelines.
YOLOv8 object detection, transfer learning with ResNet/Vision Transformers, and video stream inference.
Whisper automatic speech recognition, Q-learning, Policy Gradients, and robotic kinematic control.
Multi-agent LangGraph workflows, RAG knowledge retrieval, vector search, and tool invocation.
Fairlearn algorithmic bias auditing, toxicity red teaming, model cards, and AI safety governance.
Docker containerization, FastAPI serving, Prometheus drift monitoring, and GitHub Actions CI/CD.