A bioinformatics scientist turns messy biological data (sequencing reads, expression tables, protein data) into answers a biologist or clinician can act on. Most people in the role hold a master's or PhD, but what gets you hired is proof you can do three things: write reliable code, understand the biology, and reason with statistics. This roadmap builds them in that order. You start with Linux, Python/R and core molecular biology, then run real pipelines (RNA-seq, variant calling) with workflow tools, and finally move to method development, machine learning and cloud-scale work. Everything use
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Stage 1 of 3 · about 14 weeks
Be comfortable on the command line, write small Python and R scripts, and explain how DNA becomes RNA becomes protein and how we measure each step.
Linux command line and Git
Almost all bioinformatics tools run in a terminal on servers or clusters, not in point-and-click programs. Git lets you track your work and share it, which employers expect.
Python programming for data
Python is the general-purpose glue of the field: parsing files, building tools, and later machine learning. Learning loops, functions, dictionaries and pandas lets you handle tables of results.
R and basic statistics
Bioconductor, the main toolkit for genomics statistics, is built on R. You also need to know what a p-value, a distribution and a confidence interval actually mean before you trust any result.
Molecular biology and genomics basics
If you do not understand what a read, an exon, a variant or a transcript is, you cannot tell a real result from an artifact. The biology is what makes you a scientist and not only a coder.
Public biological databases and sequence alignment
Much of the job is finding and reusing public data. Alignment (BLAST, dynamic programming) is the core idea behind comparing sequences.
Then 2 more stages (about 58 weeks in all) with lessons, drills, projects, 6 interview questions with strong answers and free resources.