Research creates knowledge. Industry creates impact.
Your ability to convert knowledge into impact depends on both technical capability and human capability.
1
Welcome & Context
Are grades and code enough for a career in tech?
Twenty years ago, being technically strong could take you a long way. Today, technical skills are necessary — but they are no longer sufficient.
Technology changes rapidly. The ability to learn, adapt and work with people is therefore becoming as important as what you know today.
The changing technology landscape
Cloud Computing
Data Engineering
Artificial Intelligence / ML
Generative AI
Agentic AI
Cybersecurity
Quantum Computing
Edge Computing
Automation
Software Engineering
Digital Platforms
Technology changes rapidly — learning, adaptability and people skills are becoming as important as current knowledge.
2
What Does "Industry Ready" Mean?
Being industry-ready does NOT mean…
- Knowing 10 programming languages
- Having 20 certifications
- Memorising frameworks
- Getting a high academic score
Industry readiness =
Technical competence + Problem-solving + Communication + Collaboration + Adaptability + Professional responsibility
A useful model for the students
Industry readiness
Coding • Data • Cloud • AI/ML • Security • DevOps
Communication • Teamwork • Leadership • Adaptability
↓
Business impact
3
Technical Skills: Build Strong Foundations
"Frameworks change. Fundamentals stay."
Layer 1 — CS Fundamentals
- Data structures & algorithms
- Operating systems
- Computer networks
- Databases
- Object-oriented programming
- Software engineering
- Distributed systems
- Security fundamentals
Layer 2 — Programming
Python · C/C++ · C# · Java · Go · JavaScript/TypeScript
Become genuinely comfortable with at least one language. Don't learn a language merely to add it to your résumé. Learn it well enough to solve problems.
Layer 3 — Today's Technology Landscape
Cloud
Azure · AWS · Google Cloud
Data
SQL · Data engineering · Data analytics
AI
Machine learning · Generative AI · LLMs
Engineering
Git · CI/CD · APIs · DevOps
Security
Identity · Application security · Responsible AI
"Don't chase every technology. Understand the underlying concepts and learn how to learn new technologies."
4
AI Changes the Meaning of "Technical Skill"
AI will not simply replace programmers
Instead, the nature of programming and technology work is changing. You need to learn how to:
Use AI coding assistants
Prompt effectively
Review AI-generated code
Test AI-generated solutions
Understand hallucinations
Validate sources
Protect confidential info
Design AI-enabled applications
Build agentic workflows
"If AI can generate the code, your value increasingly comes from knowing what code should be generated, why it should be generated, and whether it is actually correct."
5
Projects > Certificates
Certificate says:
"I completed a course."
Projects demonstrate:
"I can apply what I learned."
Build 3–4 meaningful projects, rather than collecting dozens of certificates:
Beginner
Student management system
Intermediate
Cloud-hosted application with API + database
Advanced
AI-powered application using an LLM
Research-oriented
Prototype based on a research problem
Put the project on: GitHub · Portfolio · LinkedIn · Resume
6
Soft Skills Are Actually Career Skills
Professional and Human Skills — Career Skills
-
1
Communication
Can you explain a complex technical problem to a non-technical person?
-
2
Collaboration / Team-Work
Can you work with developers, testers, architects, managers, customers, business teams?
-
3
Problem solving
Can you break an ambiguous problem into smaller problems?
-
4
Critical thinking
Can you challenge assumptions?
-
5
Listening
Can you understand the problem before proposing a solution?
Adaptability, ownership and leadership
Adaptability
Can you learn something completely new?
Ownership
Do you say: "That's not my job" or "Let me understand the problem and see how I can help."
Leadership — not only about becoming a manager
Taking ownership · Helping teammates · Mentoring others
Presenting ideas · Resolving conflicts · Driving a project forward
7
The Most Underrated Skill
Learning how to learn
"Your degree gets you started. Your ability to learn keeps you relevant and going."
LEARN
→
PRACTICE
→
BUILD
→
EXPLAIN
→
APPLY
→
REFLECT
↻
↻ loops back to LEARN
Don't just consume courses. Learn → build → fail → debug → explain → improve.
8
Research & Industry: Don't Treat Them as Opposites
What is possible? How does it work? How does it scale?
Research asks
"What is possible?"
Engineering asks
"How can we make it work?"
Industry asks
"How can we make it work reliably, securely, economically and at scale?"
RESEARCH
→
New Knowledge
→
PROTOTYPE
→
ENGINEERING
→
SCALABILITY
→
BUSINESS / SOCIAL IMPACT
"Don't stop at publishing the idea. Ask what problem the idea can solve and work on it."
9
The 90-Day Challenge
What should a student do from tomorrow?
1
Month 1: Strengthen
- One programming language
- SQL
- Git
- CS fundamentals
2
Month 2: Build
- Create one meaningful project
- Cloud
- API
- Database
- AI where appropriate
3
Month 3: Demonstrate
- Put it on GitHub
- Document it
- Create a technical presentation
- Explain the architecture
- Present it to someone
- Add it to your resume
Throughout the 90 days: Read → Build → Discuss → Present → Improve
10
Final Gist
The 5 things I would ask you to remember
-
1
Build strong fundamentals.
-
2
Learn modern technologies — but don't become dependent on technologies.
-
3
Build things; don't merely collect certificates.
-
4
Learn to adapt, communicate, collaborate and lead.
-
5
Become a lifelong learner.
"Your career will not be defined by the technologies you know today. It will be defined by your ability to learn tomorrow's technologies, solve meaningful problems, work with people and create impact."
"Don't prepare yourself merely for your first job. Prepare yourself for a career that may last forty years."
Santosh Vellala
Technical Director, Himansu IT Services Pvt. Ltd. · Ex-Microsoft