Start where you are. Build toward what is next.
The same four levels serve three kinds of learner. Here is how the path differs for each, so you can see which one you are.
01 / BEFORE YOUR FIRST ROLEStudents
Connect what you study with what you can do. Explore a direction, work through recommended resources and build practical projects with feedback.
Find your direction
02 / AFTER YOUR DEGREEFreshers
If job descriptions feel far from what you studied, start by understanding the gap. Get guidance on what to learn and put it to work through reviewed projects.
Find your direction
03 / FOR YOUR NEXT CHAPTERWorking professionals
Build on your experience as tools and roles evolve. Explore a new direction, learn through recommended resources and practise with a mentor reviewing your work.
Find your directionHow the three paths differ.
| Students | Freshers | Working professionals | |
|---|---|---|---|
| Usual starting level | Level 1 (Foundation, 3 months) | Level 1 (Foundation, 3 months), then Level 2 for a full project and mock interviews | Level 1 (Foundation, 3 months); the team may suggest a quicker route through it if you already have relevant experience |
| Tracks that usually fit | Data Analytics, AI and Generative AI, Cloud Computing, Business Analytics | Data Analytics, Business Analytics, Cloud Computing, AI and Generative AI | AI Automation, AI and Generative AI, Cloud Computing, DevOps, Data Science |
| Time | Independent study plus one project; fits around classes. The team agrees the pace with you before you enrol. | Treat it like a part-time job: regular study, one project at a time, reviews as you submit. The pace is agreed with you on the call. | Asynchronous by design: you submit work when you have it and get written feedback, so most of the mentorship fits around a job. Session timing is agreed with you before you enrol. |
| What you leave with | One finished, reviewed project you can explain in an internship or placement conversation, and a plan for what to learn next. | A project you can defend in an interview, a resume that describes it accurately, and practice answering the questions interviewers ask about it. | A project in the new direction that connects to what you already do, reviewed by someone who works in it, and a plan for the transition. |
| Fee | ₹30,000 per level including GST, one level at a time | ₹30,000 per level including GST, one level at a time | ₹30,000 per level including GST, one level at a time |
Which tracks suit your degree or job?
Nine tracks is a lot to compare. Start from what you have already studied or done.
| Your background | Tracks that usually fit |
|---|---|
| BCom, BBA, BA, MBA or any commerce and management degree | Business Analytics, Data Analytics |
| BSc, BCA or a science degree without much coding | Data Analytics, AI and Generative AI, AI Automation |
| CS, IT or engineering, with some coding | Machine Learning, Data Science, DevOps, Cloud Computing |
| Working in support, operations, admin or IT helpdesk | AI Automation, Cloud Computing, Cybersecurity |
| Working in marketing, content, sales or HR | AI and Generative AI, AI Automation, Business Analytics |
| Working in testing or development | DevOps, AI and Generative AI, Machine Learning |
| Working in finance, accounts or reporting | Data Analytics, Business Analytics, Data Science |
A starting point, not a rule. The team suggests a track on the call, and the optional Career Audit gives you a written view.
The edge cases, answered.
Final-year student: start as a student now; the mock interviews at Level 2 line up with placement season. Gap-year graduate: you are a fresher; a finished project matters more than the gap. Non-CS graduate: look at Business Analytics or Data Analytics first. In a non-IT job: you are a working professional; AI Automation and Data Analytics build on what you already do.
Still not sure which path?
Enquire and say so. The team suggests a path on the call; nothing is sold without your agreement.
Enquire now