BTW: DO NOT start with an AIML course, starting me hii.
Coding fundamentals first, then the biology specific data science study/course, and then ML gets added later only if your chosen bioinformatics role actually needs it (e.g. drug discovery AI, protein structure work).Confirmation on the points you said:
- You said Python is "must” - correct ✔️
- You’ll need to learn data science - correct ✔️
- bioinformatics is data science applied to biological data.
Things that you need to Learn (The Coding part explained in relating with your field):
Tool/Language | What it's actually for in bioinformatics |
Python | Automation, data wrangling (pandas), building pipelines, machine learning (scikit-learn), sequence analysis (Biopython). |
R | Statistics-heavy analysis, especially genomics-specific packages (Bioconductor) - gene expression analysis, RNA-seq, visualization. |
SQL (Database Management) | Data management specifically - querying and organizing large structured datasets (patient records, sequence databases, lab results). *The Data Management skill* |
Some more tools and concepts: Linux/Bash (command line), Git/Github
First start with Python then; R and SQL…so on.. you yourself will figure out on the way!
Roadmap:
(I will attach links to tutorials and lectures for all in separate section further in this doc 👇🏻 & will keep on updating the doc as well jaise jaise shi resources yaad aate jaenge aur milte jaenge..)
- Python basics - variables, loops, functions, lists/dictionaries….
- Command line/ Linux basics - non-negotiable for real bioinformatics work later.
- SQL basics - querying/filtering data.
- Statistics refresher: descriptive stats, hypothesis testing, distributions
- pandas + numpy + matplotlib : actual data handling/analysis in Python
- ⭐Genomic Data Science Specialization (JHU, Coursera) : ⭐ the is the core course: teaches Python, R, Linux, and data management together using real biological data!!!
- Rosalind.info practice problems - alongside step 6, to build coding fluency through bio-specific exercises
- Biopython: Python library made for handling sequence data
- Biological databases: learn to actually query/use NCBI, GenBank, UniProt, PDB
- Bioconductor (R): genomics-specific statistical analysis
- Git/GitHub: start putting small projects up as a portfolio - OPTIONAL - will explain you later when time comes.
Once you reach this point:
- AT THIS POINT : Pick specialization lane (analyst / computational biologist / bioinformatics engineer / genomics data scientist…. etc..your decision).
- ML/AI course: only if your chosen lane needs it (eg. drug discovery, structure prediction).