"Learning Programming" matching MCP servers:

Read-only FastMCP server providing machine learning stock selection scores, Alpha158 signals, and SHA-256 checksums from Qlib.

Enables LLMs to generate visual summary reports from CSV datasets using MCP Resources, Tools, and Prompts, without exposing raw data to the model.

Enables exploratory data analysis and machine learning on CSV datasets with tools for profiling, missing values, correlation, plotting, model training, and prediction.

Educational MCP server with 17 tools for mathematical calculations, matrix operations, statistical analysis, and data visualization. Includes persistent workspace, cloud hosting, and safety features like restricted eval.

AI-powered MCP server for connecting and managing Jupyter Notebooks. Enables interactive code execution, multi-notebook management, and multimodal output for data analysis, visualization, and machine learning.

Enables models to render verified paper maps, section flows, concept diagrams, formula structures, and evidence prisms as real PNG images through local stdio MCP read-only tools.

Enables interactive learning and visualization of Binary Search Trees and AVL Trees through operations like insertion, deletion, and tree traversal, with real-time balancing rotations.

Enables AI assistants to perform data analysis on local datasets through natural language, orchestrating Python, R, SQL, Spark, Jupyter, Power BI, and Tableau engines for statistics, machine learning, dashboard generation, and batch processing.

Enables building configurable workplace dashboards with eight MCP apps/widgets for agenda, brief, attention, goals, leave, learning, updates, and actions, all backed by synthetic live data via a standard MCP server.

Enables Claude Code to run GNU Octave code for numerical computation, plotting, and deep learning inference via system calls.

This MCP server provides a web-ready SSE bridge for Microsoft's Power BI Modeling MCP Server, enabling URL-based interaction with Power BI semantic models from any programming language without local stdio piping. It features automatic Azure authentication and dynamic binary resolution for seamless Power BI data analysis.

Enables local offline machine learning workflows, including CSV profiling and cleaning, classifier and neural network training, hyperparameter optimization, evaluation metrics, confusion matrix plots, Streamlit dashboards, and HTML reports through MCP tools.

Builds importable RUCKUS One Data Studio dashboards from a declarative spec, enabling users to create valid dashboards without learning Superset internals or guessing field names.