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Fresher-focused program
Industry-Ready System Design & DSA Program
System Design & DSA Program
Learn Beyond Coding. Think Like an Engineer.
5.0
5.0
4.6
Build Industry-Ready Engineering Skills — System Design, Backends & DSA
Learn the Skills Required for Current Industry Expectations
Today’s software industry is evolving rapidly. Companies are no longer looking for candidates who only know coding — they expect engineers who can understand real-world application architecture, scalability, APIs, databases, cloud platforms, and modern development practices.
Our System Design program helps freshers build the practical skills that modern companies expect from software engineers, along with essential Data Structures and Algorithms foundations.
In the AI era, coding alone is no longer enough. While AI can assist in generating code, it cannot replace engineers who can think logically, design scalable systems, solve real-world problems, make architectural decisions, and build reliable applications for businesses.
This program focuses on developing the engineering mindset, problem-solving ability, and system-thinking skills that remain highly valuable and future-proof in the evolving technology industry.
From Classroom Code to Production Systems
Most freshers know programming syntax, but struggle to understand how real-world software systems are designed, scaled, secured, and maintained in production environments.
- Syntax without system context
- Little exposure to production realities
This program bridges that gap by helping students develop practical engineering thinking, architecture understanding, and industry-level backend system knowledge required in modern software companies.
Program Overview
This program is specially designed for freshers who want strong foundations in scalable application architecture, backend engineering concepts, distributed systems basics, and real-world product engineering. Unlike traditional theoretical courses, this program focuses on:
Industry-oriented system design concepts
Real-world architecture understanding
Backend scalability fundamentals
API and database design
Cloud and microservices basics
Product company interview preparation
This course transforms freshers from coders into engineering thinkers.
Traditional Coding Courses vs This Program
A quick contrast—what changes when the goal is production-ready engineering, not just completing tutorials.
| Traditional Courses | This Program |
|---|---|
| Focus on syntax | Focus on engineering thinking |
| CRUD projects | Scalable backend systems |
| Framework-centric | Architecture-centric |
| Tutorial learning | Real-world system understanding |
| Coding only | System design + scalability + cloud |
| Short-term preparation | Long-term engineering growth |
Explore the full course curriculum below.
Course Duration
- 8-week intensive program
- Weekday + weekend practice sessions
- Total duration: 80–100 hours
Who Can Join?
- Final-year students
- Fresh graduates
- Junior developers & interns
- Full stack & backend beginners
- Students preparing for product companies
Prerequisites
Basic knowledge of programming concepts, APIs, databases, and any one language:
- Java
- C#
- Python
- JavaScript
Enterprise-Style Delivery
How Real Teams Ship Software
A simple workflow used across product companies—from clarifying what to build, to collaborating in Git & GitHub, to running safely in production.
You’ll map course topics—APIs, databases, cloud, messaging—to stages like these so interviews and day-one work feel familiar.
Your Learning Path
Program Objectives
After completing this course, students will:
- Understand how large-scale applications work
- Design scalable backend systems
- Understand distributed systems fundamentals
- Design APIs and databases properly
- Learn cloud-native application basics
- Build architecture thinking ability
- Crack entry-level system design interviews
- Understand real-world software engineering
11 Modules · Capstone · DSA Lab
System Design & DSA Project-Oriented Course Curriculum
You will be exposed to the following System Design & DSA program content
- What Is System Design
- Importance of system design
- Real-world application architecture
- Product engineering overview
- Types of Applications
- Web applications
- Mobile backend systems
- Enterprise applications
- SaaS applications
- Functional vs Non-Functional Requirements
- Scalability, reliability, availability
- Performance & security
- Basic Architecture Concepts
- Client-server architecture
- Monolith architecture
- Layered architecture
- Hands-On
- Requirement analysis workshop
- Basic architecture drawing exercises
- Backend Architecture Basics
- Controllers, services, repositories
- Clean code practices
- REST APIs
- HTTP methods, status codes, CRUD
- API versioning & pagination
- Authentication Basics
- JWT introduction
- Session-based authentication
- OAuth overview
- API Design Best Practices
- Naming conventions, error handling, response structures
- Hands-On
- Design REST APIs
- Build scalable API flow diagrams
- SQL Databases
- Tables, relationships, joins
- Normalization & indexes
- NoSQL Introduction
- MongoDB basics
- Document & key-value databases
- Database Design
- ER diagrams
- One-to-many & many-to-many relationships
- Data modeling
- Database Scaling Basics
- Replication basics, sharding introduction, read replicas
- Hands-On
- Design databases for real applications
- Query optimization basics
- Understanding Scalability
- Vertical & horizontal scaling
- Stateless applications
- Load Balancing
- Why load balancers are needed
- Reverse proxy basics, traffic distribution
- Caching Basics
- Browser & server-side caching
- Redis introduction
- CDN Basics
- Static content delivery, edge servers
- Hands-On
- Scalability design exercises
- Caching implementation concepts
- Distributed Systems Basics
- What distributed systems are
- Communication between services, scalability challenges
- Microservices Introduction
- Monolith vs microservices
- Service separation, API communication
- Messaging Systems Basics
- Queues, Kafka & RabbitMQ basics
- Asynchronous processing
- Reliability Concepts
- Retry mechanisms, fault tolerance
- Circuit breaker introduction
- Hands-On
- Event-driven architecture diagrams
- Queue-based workflow design
- Cloud Infrastructure Fundamentals
- IaaS, PaaS, SaaS
- AWS & Azure overview
- Docker Foundations
- Containers, images, Docker workflow
- Kubernetes for Scalable Applications
- Pods, deployments, services
- CI/CD Basics
- GitHub Actions overview
- Deployment pipeline basics
- Hands-On
- Container architecture understanding
- Cloud deployment flow walkthrough
- Application Security Fundamentals
- SQL injection, XSS basics
- Authentication security
- Performance Basics
- Latency, throughput, response time, bottlenecks
- Observability & Monitoring Foundations
- Logging, metrics, error tracking
- Hands-On
- Security review exercises
- Performance analysis workshop
- Beginner-Friendly Designs
- URL shortener, chat application, notification service
- Food delivery, online learning platform, movie booking
- Product Architecture Understanding
- WhatsApp, Netflix, Instagram feed basics
- E-commerce application flow
- Hands-On
- Whiteboard architecture practice
- System breakdown exercises
- System Design Interview Basics
- How interviews are conducted
- Requirement gathering & thinking process
- Communication Skills
- Explaining architecture, diagrams, trade-offs
- Common Beginner Questions
- Design a chat app, notification system, file upload system
- Mock Sessions
- Whiteboard sessions, architecture discussions, peer reviews
- DS Basics
- What is DSA? Types of data structures & algorithms
- Core Topics
- Arrays & hashing, linked lists, stacks & queues
- Trees, graphs, searching & sorting
- Interview-based coding questions
- Using AI tools responsibly in development
- Safety, privacy, and intellectual-property considerations
- Human review, attribution, and team conventions
- Understanding limitations of AI-generated code
- Hallucinations, stale training, and missing context
- When generated code needs verification or replacement
- Debugging AI-generated solutions
- Reproducing failures, narrowing blame, and fixing root causes
- Using tests, logging, and diff review as guardrails
- Architecture validation
- Checking AI-suggested designs against requirements and constraints
- Trade-offs, failure modes, and operational fit
- Engineering decision making
- Deciding when AI assists vs when human judgment leads
- Recording rationale for maintainability and audits
- AI-assisted productivity workflows
- IDE assistants, refactor loops, and repeatable prompts
- Fitting AI into code review, CI, and team rituals
- Writing better prompts for development
- Clear goals, constraints, examples, and iteration
- Structured prompts for design, implementation, and review
- Using AI for documentation & testing
- Docs, comments, and onboarding materials from codebases
- Test ideas, cases, and regression checks—always validated by engineers
- Hands-On
- Review and harden a small AI-generated feature end-to-end
- Prompt-design workshop for a real bugfix or API change
- Overview
- Students design one beginner-level scalable application
- Options
- Online learning platform, food ordering system
- Student management system, chat application
- Event booking system, e-commerce backend
- Deliverables
- HLD diagram, API structure, database design
- Scalability plan & deployment flow
Unique Features of the Program
Beginner-Friendly Architecture
Complex concepts explained visually and practically.
Real Product Architecture
Learn how actual applications work internally.
Industry-Oriented Learning
Focused on real engineering practices.
Whiteboard Design Practice
Build confidence explaining systems on a whiteboard.
Architecture Thinking
Learn how engineers think at scale.
Tools & Technologies Covered
Backend Concepts
- REST APIs
- JWT
- API design
Databases
- PostgreSQL
- MongoDB
- Redis
Cloud & DevOps
- Docker
- Kubernetes
- AWS
Messaging
- Kafka
- RabbitMQ
Git & GitHub
- Git basics
- GitHub & PRs
- CI (GitHub Actions)
Practical Activities
Weekly
- Architecture assignments
- System breakdown exercises
- API design workshops
- Database modeling tasks
Group
- Design discussions
- Architecture reviews
- Whiteboard collaboration
Program at a Glance
Depth, Guided Practice, and a Capstone You Can Show
A tight curriculum: focused modules, serious contact hours, and one end-to-end design project so your skills are interview-ready.
Course Outcomes
After completion, students will:
- Understand scalable application architecture
- Design APIs and databases properly
- Understand how enterprise systems work
- Build architecture thinking ability
- Understand cloud-native fundamentals
- Gain confidence in system design interviews
- Become industry-ready backend engineers
Career Benefits
Suitable Roles:
- Backend Developer
- Junior Full Stack Developer
- Software Engineer
- API Developer
- Cloud Support Engineer
- Associate Software Engineer
