AN OPEN NOTEBOOK FOR BIOINFORMATICS
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Learning path 01 · Beginner

Build your foundations

Start with the skills your first project needs. You can learn the deeper mathematics alongside your work; completing every course here is not an entry requirement.

Beginner

Python, R, and the command line

Choose a language for a concrete task, then learn to read, transform, and visualize a small dataset.

Before you begin: No programming experience required.

What to understand

  • Python: variables, functions, tables, plotting, and small scripts.
  • R: data frames, factors, tidy data, plotting, and biological analysis packages.
  • Shell: paths, pipes, redirection, permissions, and inspecting a text file.

What you’ll be able to do

Write a short script that reads a table, checks its columns, and saves a useful summary.

Try this

Use one small table in either Python or R. Report missing values, summarize one measurement by group, and plot the result. Explain the biological meaning of each column.

Keep in mind

Choose one language for your first project. Language preference depends on the analysis and team; you do not need to learn Python and R simultaneously.

A few good resources

  • The Python Tutorial ↗

    The official language tutorial. Use it as a reference while writing small scripts rather than reading everything before practicing.

    Documentation · Beginner · Free · English
  • R for Data Science, second edition ↗

    A practical route through importing, transforming, visualizing, and communicating tabular data in R.

    Book · Beginner · Free online · English
  • Data Carpentry: genomics lessons ↗

    Learn project organization, the command line, sequence processing, and computing through biological examples.

    Tutorial · Beginner · Free · English
  • Elzero: Mastering Python ↗

    An Arabic-language programming entry point retained from the original resource list. Pair the lessons with a small biological data task.

    Course · Beginner · Free · Arabic
Beginner

Biological data and file formats

Understand what a file represents before choosing a tool to process it.

Before you begin: Basic molecular biology and the ability to inspect a text file.

What to understand

  • Sequences and reads: FASTA, FASTQ, quality scores, and reference versions.
  • Alignments and annotation: SAM/BAM, VCF, BED, and GTF/GFF; learn coordinate conventions explicitly.
  • Analysis objects: count matrices, sample metadata, AnnData, and MuData.
  • Structures: PDB and mmCIF, chains, residues, assemblies, and identifiers.

What you’ll be able to do

Explain the inputs, outputs, identifiers, and coordinate system of your chosen analysis.

Try this

Make a data dictionary for a tutorial dataset. Record its accession, organism, reference genome or structure, sample units, and file formats.

Keep in mind

Gene symbols, stable identifiers, and reference builds are not interchangeable. Preserve the versions and mapping decisions used in your analysis.

A few good resources

  • Data Carpentry: genomics lessons ↗

    Learn project organization, the command line, sequence processing, and computing through biological examples.

    Tutorial · Beginner · Free · English
  • Ensembl training ↗

    Learn to navigate genome annotation, identifiers, comparative data, and Ensembl tools using worked training materials.

    Tutorial · Beginner · Free · English
  • NCBI Learn ↗

    Find official introductions and training for NCBI databases and tools; choose a tutorial that matches your data source.

    Tutorial · Beginner · Free · English
  • AnnData documentation ↗

    Understand the annotated matrix structure used in many Python single-cell workflows, including observations, variables, and layers.

    Documentation · Intermediate · Free · English
  • PDB-101: understanding PDB data ↗

    A welcoming introduction to structure files, experimental information, assemblies, and interpretation.

    Guide · Beginner · Free · English
Beginner

Statistics and experimental design

Connect statistical decisions to the biological question and experimental units.

Before you begin: Basic tables and plots; learn probability concepts as needed.

What to understand

  • Biological versus technical replication, controls, randomization, and confounding.
  • Distributions, effect sizes, uncertainty, hypothesis tests, and multiple testing.
  • Linear models, covariates, batch effects, and exploratory data analysis.
  • For single-cell studies, identify the independent biological samples before testing.

What you’ll be able to do

Describe the comparison, independent samples, covariates, and limitations of a small study.

Try this

Sketch two experimental designs: one where treatment is confounded with batch, and one where it is balanced. Explain what can be learned from each.

Keep in mind

A small adjusted p-value does not establish a large effect or causality. Interpret effect sizes, design, and uncertainty together.

A few good resources

Beginner

Git, environments, and research habits

Make it possible for your future self and a collaborator to repeat your work.

Before you begin: A small script or notebook.

What to understand

  • Git commits, branches, readable READMEs, and reviewable changes.
  • Separate input data, code, configuration, and generated results.
  • Record package versions, random seeds, parameters, and dataset accessions.
  • Use environments first; learn containers when your workflow needs portability.

What you’ll be able to do

Share a small project with clear setup and rerun instructions.

Try this

Ask a peer to reproduce one plot using only your README. Record any missing dependency, parameter, or data-download instruction and repair it.

Keep in mind

A notebook that ran once is not sufficient documentation. Restart the session and rerun it from the beginning before sharing.

A few good resources

  • Software Carpentry: Version Control with Git ↗

    Practice tracking changes, working with remote repositories, and collaborating on a small project.

    Tutorial · Beginner · Free · English
  • Data Carpentry: genomics lessons ↗

    Learn project organization, the command line, sequence processing, and computing through biological examples.

    Tutorial · Beginner · Free · English
  • Conda: managing environments ↗

    Create and document isolated software environments, including environment files that can be shared with collaborators.

    Documentation · Beginner · Free · English
  • Quarto: getting started ↗

    Combine analysis, narrative, and outputs in a reproducible report rather than copying results into an unrelated document.

    Tutorial · Beginner · Free · English
Intermediate

Mathematics when you need it

Build intuition for the mathematics behind the methods you actually use.

Before you begin: School-level algebra; programming helps with experiments.

What to understand

  • Linear algebra: vectors, matrices, projections, eigenvectors, and PCA.
  • Probability: conditional probability, likelihood, and uncertainty.
  • Calculus and optimization: gradients, objectives, and iterative fitting.

What you’ll be able to do

Explain one method's inputs, objective, assumptions, and outputs in plain language.

Try this

Apply PCA to a small numeric table, then explain what the axes describe and how scaling changes the result.

Keep in mind

These topics are deeper study options. Do not delay an accessible first project until you have completed all of them.

A few good resources

  • MIT OpenCourseWare: Linear Algebra ↗

    A deeper route into matrices, projections, and related concepts that support PCA and machine learning.

    Course · Intermediate · Free · English
  • Statistical Thinking for the 21st Century ↗

    Develop statistical reasoning about data, probability, inference, and models before relying on a test by name.

    Book · Beginner · Free online · English
  • Dive into Deep Learning ↗

    Build conceptual and practical foundations in deep learning; select chapters relevant to the model you are studying.

    Book · Intermediate · Free online · English