Results for “visual-analysis”

Create, manipulate, and analyze complex networks and graphs with the NetworkX Python package, covering graph construction, algorithms, generators, I/O, and visualization.

Creates, manipulates, and analyzes complex networks and graphs using the NetworkX Python package, including graph algorithms, synthetic network generation, I/O, and visualization.

Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, clustering, marker gene identification, visualization, and trajectory analysis.

Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, clustering, marker gene identification, visualization, and trajectory analysis.

Decomposes trading ideas into actionable Pine Script components by analyzing YouTube videos or conceptual questions, then produces structured implementation plans.

Cleans datasets, detects anomalies, generates reports, and creates visualizations using pandas, scikit-learn, and plotting libraries to turn raw data into client-ready deliverables.

Performs geospatial vector data analysis with GeoPandas, including reading/writing shapefiles, GeoJSON, GeoPackage, and PostGIS, geometric operations, spatial joins, overlays, coordinate transformations, and map visualization.

Create, analyze, and visualize complex networks and graphs in Python with NetworkX, including graph algorithms, community detection, synthetic network generation, and multiple I/O formats.

Queries video analytics incidents, alerts, metrics, and sensor data from Elasticsearch via the VA-MCP server.

Guides data analysis, EDA, and ML tasks by teaching, diagnosing MCPs, and deciding with the user, offering multiple options and documenting decisions.

Scrapes Amazon product data from ASINs using browseract.com automation API and performs competitive analysis, comparing specifications, pricing, review quality, and visual strategies.

Perform numerical computing, matrix operations, data analysis, and scientific visualization using MATLAB or GNU Octave.

Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, clustering, marker gene identification, visualization, and trajectory analysis.

Run standard single-cell RNA-seq analysis pipelines: QC, normalization, dimensionality reduction, clustering, differential expression, and visualization using Scanpy.

Analyzes uploaded data files with full Python access, producing cleaned datasets, statistical reports, charts, and dashboards via the CellCog coding agent.

Generate comprehensive, professional-grade market research reports (50+ pages) with LaTeX formatting, strategic frameworks, and data-driven visualizations.

Perform differential gene expression analysis for bulk RNA-seq data using PyDESeq2, supporting formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.

Performs differential expression analysis on label-free quantitative (LFQ) proteomics data from MaxQuant and DIA-NN outputs, including preprocessing, imputation, statistical testing, and visualization.

Runs standard single-cell RNA-seq analysis with Scanpy, covering QC, normalization, dimensionality reduction, clustering, marker identification, visualization, and conversion of R single-cell formats to h5ad.

Generates a polished, leadership-ready performance briefing with KPI tiles, executive narrative bullets, and a driver analysis — all as a print-ready HTML document.

Build and analyze phylogenetic trees using MAFFT, IQ-TREE 2, and FastTree, with visualization via ETE3 or FigTree for evolutionary analysis, microbial genomics, viral phylodynamics, and molecular clock studies.