{"id":50979531,"url":"https://github.com/jayemscript/lab-to-code","last_synced_at":"2026-06-19T12:34:30.843Z","repository":{"id":352758774,"uuid":"1216504921","full_name":"jayemscript/lab-to-code","owner":"jayemscript","description":"A complete Python learning roadmap for scientists and researchers — covering data science, biology, chemistry, physics, and mathematics with curated libraries, tools, and 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🐍 Python for Science — Libraries \u0026 Learning Roadmap\n\n\u003e A complete guide for data science, biology, chemistry, physics, mathematics, and research using Python and computer science.\n\n---\n\n## 📦 Python Libraries by Field\n\n### 🔢 Core Data Science\n\n| Library | Purpose |\n|---|---|\n| `numpy` | Numerical computing, arrays, linear algebra |\n| `pandas` | Data manipulation, DataFrames, CSV/Excel handling |\n| `scipy` | Scientific algorithms, statistics, optimization |\n| `statsmodels` | Statistical modeling, hypothesis testing |\n\n---\n\n### 📊 Data Visualization\n\n| Library | Purpose |\n|---|---|\n| `matplotlib` | Base plotting library |\n| `seaborn` | Statistical visualization (built on matplotlib) |\n| `plotly` | Interactive charts and dashboards |\n| `bokeh` | Interactive web-ready visualizations |\n| `altair` | Declarative statistical visualization |\n\n---\n\n### 🤖 Machine Learning \u0026 AI\n\n| Library | Purpose |\n|---|---|\n| `scikit-learn` | Classical ML (regression, classification, clustering) |\n| `xgboost` | Gradient boosting (structured/tabular data) |\n| `lightgbm` | Fast gradient boosting (large datasets) |\n| `tensorflow` / `keras` | Deep learning (Google ecosystem) |\n| `pytorch` | Deep learning (research-favored) |\n| `huggingface transformers` | Pre-trained NLP and vision models |\n\n---\n\n### 🔬 Biology \u0026 Bioinformatics\n\n| Library | Purpose |\n|---|---|\n| `biopython` | DNA/RNA/protein sequences, BLAST, GenBank |\n| `scanpy` | Single-cell RNA sequencing analysis |\n| `pydeseq2` | Differential gene expression |\n| `pysam` | Reading SAM/BAM sequencing files |\n| `dendropy` | Phylogenetic trees and evolutionary analysis |\n| `rdkit` | Molecular informatics (also used in chemistry) |\n\n---\n\n### ⚗️ Chemistry\n\n| Library | Purpose |\n|---|---|\n| `rdkit` | Cheminformatics, molecular structures and fingerprints |\n| `ase` | Atomistic simulation environment |\n| `pyscf` | Quantum chemistry calculations |\n| `openbabel` | Chemical file format conversion |\n| `chempy` | Chemical kinetics and equilibrium |\n| `mendeleev` | Periodic table data and element properties |\n\n---\n\n### ⚛️ Physics\n\n| Library | Purpose |\n|---|---|\n| `sympy` | Symbolic mathematics and physics equations |\n| `astropy` | Astronomy and astrophysics data processing |\n| `qiskit` | Quantum computing (IBM framework) |\n| `cirq` | Quantum computing (Google framework) |\n| `fenics` | Finite element method for PDEs |\n| `meep` | Electromagnetic simulations (FDTD) |\n\n---\n\n### 📐 Mathematics\n\n| Library | Purpose |\n|---|---|\n| `sympy` | Symbolic math — algebra, calculus, ODEs |\n| `numpy` | Numerical linear algebra |\n| `scipy.optimize` | Optimization and root finding |\n| `scipy.integrate` | Numerical integration |\n| `networkx` | Graph theory and network analysis |\n| `cvxpy` | Convex optimization |\n\n---\n\n### 📋 Research \u0026 Data Management\n\n| Library | Purpose |\n|---|---|\n| `jupyter` | Interactive notebooks for research |\n| `papermill` | Parameterized and automated notebooks |\n| `pydantic` | Data validation and settings management |\n| `sqlalchemy` | Database interaction |\n| `h5py` | HDF5 file format for large datasets |\n| `zarr` | Chunked array storage for large scientific data |\n| `dask` | Parallel computing for big datasets |\n\n---\n\n## 🗺️ Learning Roadmap\n\n### Stage 1 — Python Fundamentals\n\u003e **Timeline: 4–8 weeks**\n\nBefore anything scientific, learn Python itself.\n\n- Variables, data types, conditionals, loops\n- Functions and scope\n- Object-Oriented Programming (classes, inheritance)\n- File I/O, error handling, virtual environments\n- Recommended resource: [Python.org Tutorial](https://docs.python.org/3/tutorial/) or *Automate the Boring Stuff with Python*\n\n---\n\n### Stage 2 — Core Scientific Stack\n\u003e **Timeline: 4–6 weeks**\n\nThese three libraries underpin every scientific field in Python.\n\n- **`numpy`** — arrays, matrix operations, broadcasting, random numbers\n- **`pandas`** — DataFrames, filtering, groupby, merging, reading CSV/Excel\n- **`matplotlib`** — line plots, scatter plots, histograms, subplots\n- Recommended resource: *Python for Data Analysis* by Wes McKinney\n\n---\n\n### Stage 3 — Data Science Core\n\u003e **Timeline: 6–10 weeks**\n\nBuild your data science foundation before specializing.\n\n- **`scikit-learn`** — regression, classification, clustering, model evaluation\n- **`seaborn`** — statistical plots, heatmaps, pair plots\n- **`statsmodels`** — hypothesis testing, linear models, time series\n- **`jupyter`** — notebooks for reproducible, shareable research workflows\n- Recommended resource: *Hands-On Machine Learning* by Aurélien Géron\n\n---\n\n### Stage 4 — Choose Your Field\n\nPick the track(s) relevant to your goals. All build on Stages 1–3.\n\n---\n\n#### 📐 Mathematics Track\n\n- Learn `sympy` for symbolic algebra, calculus, ODEs, and proofs\n- Use `scipy.integrate` and `scipy.optimize` for numerical methods\n- Use `networkx` for graph theory problems\n- Use `cvxpy` for convex optimization\n- Projects: solve differential equations, visualize mathematical surfaces, implement graph algorithms\n\n---\n\n#### 🔬 Biology / Bioinformatics Track\n\n- Learn `biopython` — parse GenBank files, run BLAST, handle sequences\n- Learn `scanpy` — single-cell RNA-seq clustering and visualization\n- Use `dendropy` for phylogenetic trees\n- Projects: analyze a genome, build a phylogenetic tree, perform differential expression analysis\n\n---\n\n#### 🤖 Data Science / AI Track\n\n- Learn `pytorch` or `tensorflow` for neural networks\n- Learn `huggingface transformers` for working with LLMs and NLP\n- Learn `xgboost` / `lightgbm` for tabular data competitions\n- Projects: image classifier, text sentiment model, fine-tune a language model\n\n---\n\n#### ⚗️ Chemistry Track\n\n- Learn `rdkit` — draw molecules, compute fingerprints, similarity search\n- Learn `ase` — molecular dynamics simulations\n- Learn `pyscf` — quantum chemistry (Hartree-Fock, DFT)\n- Projects: build a molecular similarity search, simulate crystal structure, compute molecular properties\n\n---\n\n#### ⚛️ Physics Track\n\n- Learn `sympy` — solve physics equations symbolically\n- Learn `astropy` — process astronomical data and coordinate systems\n- Learn `qiskit` — build and simulate quantum circuits\n- Learn `fenics` — solve PDEs with finite element method\n- Projects: simulate planetary motion, solve the heat equation, run a quantum algorithm\n\n---\n\n### Stage 5 — Advanced \u0026 Interdisciplinary\n\u003e **Timeline: ongoing**\n\nWhere fields converge and real breakthroughs happen.\n\n- **Computational biology** — use ML on genomic data, protein structure prediction (AlphaFold), drug target identification\n- **AI for science** — machine learning for materials discovery, reaction prediction, climate modeling\n- **Scientific ML / PINNs** — physics-informed neural networks that embed physical laws into model architecture\n- **Quantum ML** — hybrid classical-quantum algorithms with `qiskit` or `cirq`\n\n---\n\n### Stage 6 — Real-World Research Skills\n\u003e **Timeline: ongoing alongside Stages 4–5**\n\nThese skills make your work production-quality and publishable.\n\n- **`git` + GitHub** — version control for code and data pipelines\n- **Virtual environments** (`venv`, `conda`) — reproducible dependency management\n- **`dask` / `zarr` / `h5py`** — handling datasets too large for RAM\n- **`papermill`** — automated, parameterized notebook execution\n- **Docker** — containerize your research environment\n- **Zenodo / arXiv** — publish datasets and preprints openly\n\n---\n\n## ⏱️ Realistic Timeline\n\n| Goal | Estimated Time |\n|---|---|\n| Python basics (Stage 1) | 4–8 weeks |\n| Core scientific stack (Stage 2) | 4–6 weeks |\n| Data science core (Stage 3) | 6–10 weeks |\n| One field specialization (Stage 4) | 3–6 months |\n| Advanced interdisciplinary work (Stage 5+) | Ongoing |\n\n---\n\n## 🧭 Recommended Learning Path (Quick Reference)\n\n```\nPython basics\n    ↓\nnumpy + pandas + matplotlib\n    ↓\nscikit-learn + seaborn + Jupyter\n    ↓\n┌──────┬────────┬──────────┬──────────┬────────┐\nMath  Biology  Data/AI  Chemistry  Physics\n    ↓\nAI for Science / Scientific ML\n    ↓\nReproducibility + Big Data + Publishing\n    ↓\nIndependent Researcher / Scientist\n```\n\n---\n\n## 📚 Recommended Resources\n\n- [Python.org Official Tutorial](https://docs.python.org/3/tutorial/)\n- [Kaggle Learn](https://www.kaggle.com/learn) — free, hands-on data science courses\n- [fast.ai](https://www.fast.ai/) — practical deep learning\n- [Rosalind](https://rosalind.info/) — bioinformatics problems in Python\n- [Qiskit Textbook](https://qiskit.org/learn) — quantum computing\n- *Python for Data Analysis* — Wes McKinney\n- *Hands-On Machine Learning* — Aurélien Géron\n- *Introduction to Bioinformatics* — Arthur Lesk\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjayemscript%2Flab-to-code","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjayemscript%2Flab-to-code","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjayemscript%2Flab-to-code/lists"}