Software & Computer Programming

Research-grade code, pipelines & reproducible computing

We build robust scripts, applications, and workflows for data collection, processing, analysis, simulation, and visualization documented for long-term maintenance and reproducibility.

Clean, tested code Automated pipelines Reproducible outputs

What’s included

Requirements & design-use cases, I/O schema, performance/latency goals, security notes.
Implementation-clean, modular code with docstrings/comments and version control.
Testing-unit tests, sample data, edge-case handling; CI-ready if needed.
Reproducibility-env files, lockfiles, containers/notebooks, runbooks.
Documentation & handover-README, usage guides, API notes, change log.

Typical stack

Python R JavaScript/Node Bash SQL LaTeX Docker Git

Use-cases

Data pipelines Web scrapers APIs/ETL Simulations Dashboards/Apps Computational notebooks

Capabilities

Data ingestion/cleaning

Parsers for CSV/Excel/JSON/SQL; schema validation; missing/outlier rules.

APIs & web scraping

Requests/async, rate-limit handling, polite scraping, rotating proxies.

Analysis libraries

NumPy/Pandas, SciPy/statsmodels, tidyverse; custom utilities.

Automation

Makefiles/Invoke/GitHub Actions; scheduled jobs and alerts.

Visualization

Matplotlib/Plotly/ggplot; publication-ready figures and exports.

Apps & dashboards

Flask/FastAPI/Streamlit/Shiny (lightweight research tools).

Reproducibility

Docker/venv/renv; lockfiles; deterministic seeds; runbooks.

Packaging

CLI tools, wheels, internal packages with semantic versioning.

Quick reference

AreaExamplesNotes
Data IOPandas/pyarrow, readr/dbplyr, SQLAlchemyTyped schemas, validators, profiling
Testingpytest/testthatFixtures, coverage, CI runners
APIsFastAPI/Flask, PlumberAuth, pagination, rate limits
Scrapingrequests/asyncio, rvestRobots.txt respect, retries, backoff
VizMatplotlib/Plotly, ggplot2Export to PNG/PDF/SVG, journal sizes
ReproDocker/venv/renvPin deps; lockfile; checksum data

All code is versioned, documented, and delivered with runnable examples.

Engagement examples

Research Script Pack (10–15 hrs)
Starter

One-off script/notebook + docs + small test set.

Custom quote
Request proposal
Data Pipeline (25–40 hrs)
Most popular

Ingestion → cleaning → analysis + CI + runbook.

Custom quote
Request proposal
App/Dashboard (45–70 hrs)
Interactive

Lightweight API/app + auth + deploy guide + tests.

Custom quote
Request proposal

Pricing varies with complexity, integrations, and turnaround. You’ll receive a clear plan after discovery.

Process & timeline

1Discovery

Use cases, data sources, constraints, deployment target.

2Design

Architecture, tech stack, milestones, acceptance tests.

3Build

Iterative implementation with version control & demos.

4Test

Unit/integration tests, performance checks, fixes.

5Document

README, usage examples, configs, runbooks.

6Handover

Code, env files, containers, and change log.

Typical deliverables

  • Clean, modular code with docstrings and comments
  • Reproducible environment (requirements/lockfile or Dockerfile)
  • Unit/integration tests and sample datasets
  • Publication-/submission-ready outputs (figures/tables/reports)
  • README, usage guide, API notes, and change log

FAQ

Primarily Python, R, JavaScript/Node, Bash, and SQL others on request.

Yes GitHub/GitLab/Bitbucket and common branching workflows.

Yes optional; includes Dockerfile, compose examples, and usage notes.

Yes with respect for robots.txt/ToS, backoff, retries, and data quality checks.

Yes representative tests are standard; we can set up CI runners if needed.

Yes REST/GraphQL; we manage auth, pagination, and rate limits.

Yes Streamlit/Shiny/Flask/FastAPI for lightweight research tools.

You’ll receive a README, usage examples, config/env instructions, and a change log.

Small scripts: days; pipelines/apps: weeks with milestone plan.

By complexity, integrations, testing depth, and timeline; we quote after discovery.

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