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Data & Analytics
Data analysis, BI, visualization, datasets, statistics, and ML workflows
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Showing 193–216 of 12,629 skills
- StatsmodelsStatistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.Votes: 0GitHub stars: 2
- StatisticianReasons from estimands, generative-model assumptions, and a budgeted Type I/II error tradeoff through analysis plans (SAP, ICH E9(R1) estimands), mixed models, multiple imputation under MCAR/MAR/MNAR, and Benjamini-Hochberg FDR while treating naive post-selection SEs, unadjusted multiplicity, ignored clustering in survey PSUs, and sequential peeking as first-class failure modes.Votes: 0GitHub stars: 2
- Statistical ReviewerAudit statistical analyses: test choice, assumptions, power, multiplicity.Votes: 0GitHub stars: 2
- Statistical PhysicistReasons from ensembles and partition functions through finite-size scaling (Binder cumulant, data collapse), Wolff/cluster MC, and RG/MCRG to Jarzynski–Crooks fluctuation theorems; uses ALPS, NetKet, WHAM, and ED/DMRG while treating critical slowing down, subleading FSS humps, and poor work-histogram overlap as first-class failure modes.Votes: 0GitHub stars: 2
- SeabornStatistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.Votes: 0GitHub stars: 2
- Scikit BioBiological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.Votes: 0GitHub stars: 2
- Quantitative BiologistReasons from SBML/PEtab ODE models, structural and profile-likelihood identifiability, Bayesian inference (Stan/PyMC/AMICI), and live-cell pipelines (Cellpose/TrackMate/PhotoFiTT, REMBI); treats sloppiness, phototoxicity, and segmentation-tracking artifacts as first-class failure modes.Votes: 0GitHub stars: 2
- Pydeseq2Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.Votes: 0GitHub stars: 2
- Proteomics ScientistReasons from peptide-to-protein inference, acquisition mode, quantification modality, and missing-value mechanism through MaxQuant, FragPipe/MSFragger, DIA-NN/Spectronaut, Skyline, and MSstats/proDA while treating MNAR missingness, batch confounding, TMT co-isolation ratio compression, and keratin/contaminant signal as first-class failure modes.Votes: 0GitHub stars: 2
- Precision Agriculture SpecialistReasons from management-zone heterogeneity, the spatial 4R (right input, rate, place, time), and per-zone margin maps through SSURGO/ECa zone delineation, NDVI/NDRE indices, RTK-GNSS georeferencing, and ISOBUS Task Controller as-applied logs while treating planned-versus-applied divergence, NDVI saturation, miscalibrated yield-monitor mass-flow and lag, and RTK float passes as first-class failure modes.Votes: 0GitHub stars: 2
- Population GeneticistReasons from Wright–Fisher/coalescent demography, Weir–Cockerham FST, EIGENSOFT PCA, ADMIXTURE ancestry, ADMIXTOOLS f-statistics, and selscan XP-EHH/iHS/PBS selection scans while treating batch confounding, LD pruning choices, cryptic relatedness, and admixture-LD artifacts as first-class failure modes.Votes: 0GitHub stars: 2
- PolarsFast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.Votes: 0GitHub stars: 2
- Plasma PhysicistReasons from collective scales (Debye length, plasma frequency), dimensionless regime parameters (beta, collisionality, Lundquist number), and instability drive-versus-dissipation through Grad-Shafranov equilibria (EFIT, VMEC), gyrokinetic and MHD codes (GENE, NIMROD, XGC), PIC simulation (VPIC, OSIRIS), and confinement scalings (IPB98, Greenwald, Troyon) while treating probe sheath distortion, equilibrium-reconstruction error, resolution-limited reconnection rates, and unmatched wall conditi...Votes: 0GitHub stars: 2
- Photonics EngineerReasons from Maxwell modes, FSR–Q–coupling trade-offs, and optical power/loss budgets; designs PICs and free-space systems with FDTD/INTERCONNECT/Zemax/GDSFactory and certifies links with OLTS/OTDR/M² while treating mesh dispersion errors, TE/TM birefringence, APC/PC connector mismatch, OTDR ghost/gainer events, and Fabry–Pérot convolution artifacts as first-class failure modes.Votes: 0GitHub stars: 2
- Pharmacovigilance ScientistReasons from ICSR validity, MedDRA/SMQ coding, seriousness/expectedness/listedness, WHO-UMC causality, and PRR/ROR/IC/EBGM signal workflows through E2B(R3), EudraVigilance/FAERS/VigiBase, GVP Modules VI–IX, and PSUR/PBRER/RMP while treating duplicates, MLM scope, innocent-bystander confounding, and Weber/stimulated reporting as first-class failure modes.Votes: 0GitHub stars: 2
- PharmacokineticistReasons from mass balance, exposure-response, and separation of structural from statistical models through NCA in Phoenix WinNonlin, mixed-effects popPK in NONMEM, PBPK in Simcyp/GastroPlus, and VPC diagnostics while treating BLQ mishandling, ETA shrinkage, over-parameterization for small n, and unit/analyte/matrix errors as first-class failure modes.Votes: 0GitHub stars: 2
- Optical PhysicistReasons from quantized atom-field coupling, recoil and trap energy scales (Γ, E_rec, U/J, κ), and coupled instability-versus-systematic budgets through optical Bloch equations, in-situ lattice-depth and Rabi calibration, QuTiP and ARC modeling, and Allan-deviation and clock systematic tables, while treating uncalibrated lattice depth, intensity-noise heating, double-occupancy mimicking unity filling, and missing BBR or AC-Stark shifts as first-class failure modes.Votes: 0GitHub stars: 2
- Neuroimaging ScientistReasons from k-space acquisition physics, BOLD hemodynamics, and per-voxel statistical models through fMRIPrep/QSIPrep BIDS pipelines, FSL/SPM/nilearn analysis, neuroCombat harmonization, and TFCE/permutation inference while treating head motion, partial-volume and reference-region errors in PET, global-signal regression artifacts, and site over-correction as first-class failure modes.Votes: 0GitHub stars: 2
- Mining GeologistReasons from deposit-type models (porphyry, VMS, SEDEX, orogenic Au, IOCG, skarn) through oriented core logging, domaining, variography, OK/MIK/LUC estimation, and Chain-of-Mining reconciliation to JORC Table 1, NI 43-101 Item 14, and CIM MRMR reporting; uses Leapfrog, GIM Suite/MX Deposit, and Parker F-series factors while treating support/compositing errors, batch QA/QC failures, OK smoothing bias, and Inferred-overclaim as first-class failure modes.Votes: 0GitHub stars: 2
- MatplotlibLow-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.Votes: 0GitHub stars: 2
- Mathematical ModelerReasons from nondimensionalization, conservation/positivity laws, and minimal-viable model structure through mechanistic ODE/PDE, stochastic, and agent-based formulations, profile-likelihood and Fisher-information identifiability, and Sobol/Morris sensitivity analysis, while treating sloppy unidentifiable parameters, structural model uncertainty, and out-of-regime extrapolation as first-class failure modes.Votes: 0GitHub stars: 2
- Machine Learning ResearcherReasons from population risk, double descent, and inductive bias; enforces sacred test sets, hierarchical ablations, nested CV, and HELM/Dynabench-aware benchmarking; reports with NeurIPS and Pineau reproducibility checklists while treating leakage, meta-overfitting, benchmark contamination, Goodhart gaming, and seed variance as first-class failure modes.Votes: 0GitHub stars: 2
- Life Cycle Assessment AnalystReasons from functional unit, attributional-versus-consequential framing, and ISO 14044 allocation hierarchy through openLCA, SimaPro, Brightway2, ecoinvent, and LCIA methods like TRACI and EF 3.0 while treating allocation-driven ranking flips, biogenic-versus-fossil carbon mistagging, cut-off-masked hotspots, and PCR mismatch as first-class failure modes.Votes: 0GitHub stars: 2
- Information Retrieval ScientistReasons from the Probability Ranking Principle, ranked-list utility, and candidate-generation-versus-re-ranking separation through BM25 baselines, dense and cross-encoder retrieval, and TREC-style qrels evaluated with trec_eval and nDCG, while treating position-biased clicks, unjudged-as-nonrelevant pools, analyzer mismatches, and AOL-style search-log re-identification as first-class failure modes.Votes: 0GitHub stars: 2