https://github.com/jayemscript/lab-to-code
A complete Python learning roadmap for scientists and researchers — covering data science, biology, chemistry, physics, and mathematics with curated libraries, tools, and resources.
https://github.com/jayemscript/lab-to-code
bioinformatics chemistry data-science jupyter-notebook machine-learning mathematics numpy pandas physics python research roadmap scientific-computing scikit-learn
Last synced: about 21 hours ago
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A complete Python learning roadmap for scientists and researchers — covering data science, biology, chemistry, physics, and mathematics with curated libraries, tools, and resources.
- Host: GitHub
- URL: https://github.com/jayemscript/lab-to-code
- Owner: jayemscript
- Created: 2026-04-21T01:17:08.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2026-04-21T01:17:40.000Z (2 months ago)
- Last Synced: 2026-04-21T03:34:50.293Z (2 months ago)
- Topics: bioinformatics, chemistry, data-science, jupyter-notebook, machine-learning, mathematics, numpy, pandas, physics, python, research, roadmap, scientific-computing, scikit-learn
- Homepage:
- Size: 4.88 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🐍 Python for Science — Libraries & Learning Roadmap
> A complete guide for data science, biology, chemistry, physics, mathematics, and research using Python and computer science.
---
## 📦 Python Libraries by Field
### 🔢 Core Data Science
| Library | Purpose |
|---|---|
| `numpy` | Numerical computing, arrays, linear algebra |
| `pandas` | Data manipulation, DataFrames, CSV/Excel handling |
| `scipy` | Scientific algorithms, statistics, optimization |
| `statsmodels` | Statistical modeling, hypothesis testing |
---
### 📊 Data Visualization
| Library | Purpose |
|---|---|
| `matplotlib` | Base plotting library |
| `seaborn` | Statistical visualization (built on matplotlib) |
| `plotly` | Interactive charts and dashboards |
| `bokeh` | Interactive web-ready visualizations |
| `altair` | Declarative statistical visualization |
---
### 🤖 Machine Learning & AI
| Library | Purpose |
|---|---|
| `scikit-learn` | Classical ML (regression, classification, clustering) |
| `xgboost` | Gradient boosting (structured/tabular data) |
| `lightgbm` | Fast gradient boosting (large datasets) |
| `tensorflow` / `keras` | Deep learning (Google ecosystem) |
| `pytorch` | Deep learning (research-favored) |
| `huggingface transformers` | Pre-trained NLP and vision models |
---
### 🔬 Biology & Bioinformatics
| Library | Purpose |
|---|---|
| `biopython` | DNA/RNA/protein sequences, BLAST, GenBank |
| `scanpy` | Single-cell RNA sequencing analysis |
| `pydeseq2` | Differential gene expression |
| `pysam` | Reading SAM/BAM sequencing files |
| `dendropy` | Phylogenetic trees and evolutionary analysis |
| `rdkit` | Molecular informatics (also used in chemistry) |
---
### ⚗️ Chemistry
| Library | Purpose |
|---|---|
| `rdkit` | Cheminformatics, molecular structures and fingerprints |
| `ase` | Atomistic simulation environment |
| `pyscf` | Quantum chemistry calculations |
| `openbabel` | Chemical file format conversion |
| `chempy` | Chemical kinetics and equilibrium |
| `mendeleev` | Periodic table data and element properties |
---
### ⚛️ Physics
| Library | Purpose |
|---|---|
| `sympy` | Symbolic mathematics and physics equations |
| `astropy` | Astronomy and astrophysics data processing |
| `qiskit` | Quantum computing (IBM framework) |
| `cirq` | Quantum computing (Google framework) |
| `fenics` | Finite element method for PDEs |
| `meep` | Electromagnetic simulations (FDTD) |
---
### 📐 Mathematics
| Library | Purpose |
|---|---|
| `sympy` | Symbolic math — algebra, calculus, ODEs |
| `numpy` | Numerical linear algebra |
| `scipy.optimize` | Optimization and root finding |
| `scipy.integrate` | Numerical integration |
| `networkx` | Graph theory and network analysis |
| `cvxpy` | Convex optimization |
---
### 📋 Research & Data Management
| Library | Purpose |
|---|---|
| `jupyter` | Interactive notebooks for research |
| `papermill` | Parameterized and automated notebooks |
| `pydantic` | Data validation and settings management |
| `sqlalchemy` | Database interaction |
| `h5py` | HDF5 file format for large datasets |
| `zarr` | Chunked array storage for large scientific data |
| `dask` | Parallel computing for big datasets |
---
## 🗺️ Learning Roadmap
### Stage 1 — Python Fundamentals
> **Timeline: 4–8 weeks**
Before anything scientific, learn Python itself.
- Variables, data types, conditionals, loops
- Functions and scope
- Object-Oriented Programming (classes, inheritance)
- File I/O, error handling, virtual environments
- Recommended resource: [Python.org Tutorial](https://docs.python.org/3/tutorial/) or *Automate the Boring Stuff with Python*
---
### Stage 2 — Core Scientific Stack
> **Timeline: 4–6 weeks**
These three libraries underpin every scientific field in Python.
- **`numpy`** — arrays, matrix operations, broadcasting, random numbers
- **`pandas`** — DataFrames, filtering, groupby, merging, reading CSV/Excel
- **`matplotlib`** — line plots, scatter plots, histograms, subplots
- Recommended resource: *Python for Data Analysis* by Wes McKinney
---
### Stage 3 — Data Science Core
> **Timeline: 6–10 weeks**
Build your data science foundation before specializing.
- **`scikit-learn`** — regression, classification, clustering, model evaluation
- **`seaborn`** — statistical plots, heatmaps, pair plots
- **`statsmodels`** — hypothesis testing, linear models, time series
- **`jupyter`** — notebooks for reproducible, shareable research workflows
- Recommended resource: *Hands-On Machine Learning* by Aurélien Géron
---
### Stage 4 — Choose Your Field
Pick the track(s) relevant to your goals. All build on Stages 1–3.
---
#### 📐 Mathematics Track
- Learn `sympy` for symbolic algebra, calculus, ODEs, and proofs
- Use `scipy.integrate` and `scipy.optimize` for numerical methods
- Use `networkx` for graph theory problems
- Use `cvxpy` for convex optimization
- Projects: solve differential equations, visualize mathematical surfaces, implement graph algorithms
---
#### 🔬 Biology / Bioinformatics Track
- Learn `biopython` — parse GenBank files, run BLAST, handle sequences
- Learn `scanpy` — single-cell RNA-seq clustering and visualization
- Use `dendropy` for phylogenetic trees
- Projects: analyze a genome, build a phylogenetic tree, perform differential expression analysis
---
#### 🤖 Data Science / AI Track
- Learn `pytorch` or `tensorflow` for neural networks
- Learn `huggingface transformers` for working with LLMs and NLP
- Learn `xgboost` / `lightgbm` for tabular data competitions
- Projects: image classifier, text sentiment model, fine-tune a language model
---
#### ⚗️ Chemistry Track
- Learn `rdkit` — draw molecules, compute fingerprints, similarity search
- Learn `ase` — molecular dynamics simulations
- Learn `pyscf` — quantum chemistry (Hartree-Fock, DFT)
- Projects: build a molecular similarity search, simulate crystal structure, compute molecular properties
---
#### ⚛️ Physics Track
- Learn `sympy` — solve physics equations symbolically
- Learn `astropy` — process astronomical data and coordinate systems
- Learn `qiskit` — build and simulate quantum circuits
- Learn `fenics` — solve PDEs with finite element method
- Projects: simulate planetary motion, solve the heat equation, run a quantum algorithm
---
### Stage 5 — Advanced & Interdisciplinary
> **Timeline: ongoing**
Where fields converge and real breakthroughs happen.
- **Computational biology** — use ML on genomic data, protein structure prediction (AlphaFold), drug target identification
- **AI for science** — machine learning for materials discovery, reaction prediction, climate modeling
- **Scientific ML / PINNs** — physics-informed neural networks that embed physical laws into model architecture
- **Quantum ML** — hybrid classical-quantum algorithms with `qiskit` or `cirq`
---
### Stage 6 — Real-World Research Skills
> **Timeline: ongoing alongside Stages 4–5**
These skills make your work production-quality and publishable.
- **`git` + GitHub** — version control for code and data pipelines
- **Virtual environments** (`venv`, `conda`) — reproducible dependency management
- **`dask` / `zarr` / `h5py`** — handling datasets too large for RAM
- **`papermill`** — automated, parameterized notebook execution
- **Docker** — containerize your research environment
- **Zenodo / arXiv** — publish datasets and preprints openly
---
## ⏱️ Realistic Timeline
| Goal | Estimated Time |
|---|---|
| Python basics (Stage 1) | 4–8 weeks |
| Core scientific stack (Stage 2) | 4–6 weeks |
| Data science core (Stage 3) | 6–10 weeks |
| One field specialization (Stage 4) | 3–6 months |
| Advanced interdisciplinary work (Stage 5+) | Ongoing |
---
## 🧭 Recommended Learning Path (Quick Reference)
```
Python basics
↓
numpy + pandas + matplotlib
↓
scikit-learn + seaborn + Jupyter
↓
┌──────┬────────┬──────────┬──────────┬────────┐
Math Biology Data/AI Chemistry Physics
↓
AI for Science / Scientific ML
↓
Reproducibility + Big Data + Publishing
↓
Independent Researcher / Scientist
```
---
## 📚 Recommended Resources
- [Python.org Official Tutorial](https://docs.python.org/3/tutorial/)
- [Kaggle Learn](https://www.kaggle.com/learn) — free, hands-on data science courses
- [fast.ai](https://www.fast.ai/) — practical deep learning
- [Rosalind](https://rosalind.info/) — bioinformatics problems in Python
- [Qiskit Textbook](https://qiskit.org/learn) — quantum computing
- *Python for Data Analysis* — Wes McKinney
- *Hands-On Machine Learning* — Aurélien Géron
- *Introduction to Bioinformatics* — Arthur Lesk