IITM BS Big Data and Biological Networks (BSBT4002): Syllabus and Tips

Big Data and Biological Networks (BSBT4002) is a 4 credit elective at the degree level of the IITM BS in Data Science. Its code starts with 4, so it is a level 4 course. There is no prerequisite on the course page or in the handbook. You only need to have reached the degree level. The course shows you how to treat biological data as networks, analyse those networks, and apply machine learning to them in Python.

The course page lists three IIT Madras instructors: Dr. Nirav P Bhatt, Karthik Raman and Prof. Himanshu Sinha.

What you learn

• Weeks 1 and 2: what biological big data looks like. How information flows in living systems. Then the main kinds of large datasets, called omics data: genomic, transcriptomic, proteomic, metabolomic and others.
• Weeks 3 to 6: network basics. A short history of graph theory and the types of graphs. How to represent biological systems as networks. How to measure a network, including centrality. Three classic random network models: Erdos-Renyi, Watts-Strogatz (small world) and Barabasi-Albert (power law). Then finding communities, spotting repeated small patterns (motifs), and studying what happens when a network is disturbed.
• Weeks 7 and 8: uses in medicine and health. Predicting drug targets and drug molecules, and designing new molecules (chemoinformatics). Epidemiology. The centrality-lethality hypothesis, which links how central a node is to how essential it is.
• Weeks 9 to 12: machine learning on networks. Common AI and ML tasks on biological networks. Rebuilding networks from omics data and from published papers. Predicting node labels and missing links. Networks with many node types or many layers, and where the field is heading.

The course aims include writing basic network analysis algorithms in Python and applying ML to biological data in Python. The page does not name a specific library.

How it is assessed

The course page lists the standard pattern: 12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes and 1 in-person invigilated end term exam. Your term's grading document gives the weight of each part.

Where it counts

• BS level electives. The handbook tags it BD/BP. BD appears to mean the data science stream and BP the programming stream. The BS level needs 2 level 4 or higher courses in each stream, but the handbook does not say which stream a BD/BP course fills. Confirm with support. See the BS degree level.
• BSc level. The handbook's BSc fee options include level 4 electives, so you can also take it during the BSc level.
• Minors. It is not part of any minor in the handbook.
• Terms. The March 2026 course table marks it as running in September 2026 and January 2027, but not May 2026. Offering also depends on registration numbers.

Who finds it hard

This course mixes biology, graph theory and machine learning. You may be strong in one or two of these but not all three. If you have not seen biology since school, weeks 1, 2, 7 and 8 bring many new words. If your ML is rusty, weeks 9 to 12 will feel fast, because node classification and link prediction build on training and judging a classifier.

How to prepare

1. Revise graphs. Go back to the graph weeks of PDSA (BSCS2002): adjacency lists, breadth first search, shortest paths.
2. Compute centrality by hand once. Take a star network with one centre and four outer nodes. The centre has degree 4 and each outer node has degree 1. Every shortest path between two outer nodes passes through the centre, so its betweenness is the highest. Small cases like this make the formulas easy to trust.
3. Revise classification. Node classification and link prediction are classification problems. Your notes from Machine Learning Techniques (BSCS2007) help here.
4. Build a small network in Python before week 4. Load an edge list, count degrees and find the most connected nodes. Then later weeks are about ideas, not setup.
5. Keep a glossary. Write one line for each biology term and each network term as it appears.

What to take before or after

The diploma ML courses and PDSA are the best base. At the degree level, Algorithmic Thinking in Bioinformatics (BSBT4001) is the other biotechnology elective and covers sequence and string algorithms. Introduction to Big Data (BSDA5001) is another BD/BP elective, at level 5. All options are in IITM BS Data Science electives.

Common questions

Do I need a biology background for BSBT4002?

No prerequisite is listed. Weeks 1 and 2 introduce the kinds of biological data used later. If biology is new to you, learn the basic terms early so they do not slow you down.

Is BSBT4002 a machine learning course?

Partly. Weeks 9 to 12 are about AI and ML on biological networks. The first eight weeks are about network structure, network models and applications.

Can I take BSBT4001 and BSBT4002 in the same term?

Neither course lists the other as a prerequisite, so the rules do not stop you. Check that both run in that term. The handbook table shows both running in January 2027.