Pre-Internship
Training Program by Mr. Vimal Daga
With 22+ Years of Experiance
Trained 10 lakhs+ Students
Module 1: Introduction to SRE and DevOps
- Introduction to SRE:
- Definition, goals, and core principles.
- Key concepts: SLOs, SLIs, and Error Budgets.
- Case studies of successful SRE implementations.
- Introduction to DevOps:
- Definition, goals, and core practices.
- The DevOps lifecycle: Plan, Develop, Deliver, Operate, Monitor.
- Cultural aspects of DevOps: Collaboration, Automation, and Measurement.
- Integration of SRE and DevOps:
- How SRE practices can enhance DevOps.
- Collaboration between SRE and DevOps teams.
Module 2: Coding and Scripting
- Python Programming:
- Syntax and data types.
- Functions, modules, and error handling.
- Libraries: Requests, Flask, NumPy, Pandas.
- Java Programming:
- Basics: Classes, objects, inheritance.
- Common libraries and frameworks: Spring Boot.
- Exception handling and concurrency.
- Go Programming:
- Syntax, types, and functions.
- Concurrency with Goroutines and Channels.
- Standard library and packages.
- Ruby Programming:
- Basics: Classes, modules, and blocks.
- Rails framework overview.
- Common gems and usage.
- Scripting:
- Bash Scripting: Variables, control structures, functions, and automation.
- PowerShell Scripting: Cmdlets, scripts, and managing Windows environments.
Module 3: Version Control Systems
- Git Fundamentals:
- Repository initialization, commits, branching, merging.
- Rebasing, stashing, and resolving conflicts.
- Advanced Git features: Bisect, cherry-picking, and hooks.
- Version Control Platforms:
- GitHub: Repository management, pull requests, issues, and actions.
- GitLab: CI/CD pipelines, issue tracking, and merge requests.
- Bitbucket: Repository management, pull requests, and integration.
Module 4: Operating Systems
- Linux:
- System architecture, file systems, and permissions.
- Process management, package management, and networking.
- System tuning, troubleshooting, and security.
- Windows:
- System architecture, file systems, and registry.
- PowerShell scripting for automation.
- Managing Windows services, and security best practices.
Module 5: Cloud Computing
- AWS:
- Core services: EC2, S3, RDS, Lambda, IAM.
- Advanced services: CloudFormation, ECS, EKS.
- Security and cost management.
- Azure:
- Core services: VMs, Blob Storage, SQL Database, Functions.
- Advanced services: Resource Manager, AKS, DevOps.
- Security and cost management.
- Google Cloud:
- Core services: Compute Engine, Cloud Storage, BigQuery, Cloud Functions.
- Advanced services: GKE, Cloud Spanner, Cloud Build.
- Security and cost management.
Module 6: CI/CD Pipelines
- CI/CD Concepts:
- Definitions: Continuous Integration, Continuous Deployment, Continuous Delivery.
- Building effective CI/CD workflows.
- CI/CD Tools:
- Jenkins: Setting up pipelines, using plugins, and configuring jobs.
- GitLab CI: Defining pipelines, runners, and integrating with repositories.
- CircleCI: Creating workflows, configuring jobs, and deployment strategies.
Module 7: Infrastructure as Code (IaC)
- Teraform:
- Configuration files, modules, and state management.
- Advanced features: Provisioners, data sources, and workspace management.
- Ansible:
- Playbooks, roles, and inventory management.
- Managing configurations and automating deployments.
- CloudFormation:
- Writing templates, creating stacks, and managing resources.
- Advanced features: Nested stacks, change sets.
Module 8: Monitoring and Logging
- Monitoring Tools:
- Prometheus: Metrics collection, alerting, and query language.
- Grafana: Dashboard creation, data source integration, and visualization.
- Logging Tools:
- ELK Stack: Elasticsearch for indexing, Logstash for data collection, Kibana for visualization.
- Alternative Tools: Splunk, Datadog, or similar.
Module 9: Containerization and Orchestration
- Docker:
- Basics: Containers, images, and Dockerfiles.
- Advanced topics: Networking, volumes, and Docker Compose.
- Kubernetes:
- Architecture: Pods, services, deployments, and stateful sets.
Module 10: Networking
- Networking Concepts:
- IP addressing, DNS, routing, and subnetting.
- Protocols: TCP/IP, HTTP/HTTPS, UDP.
- Networking Tools:
- Wireshark: Capturing and analyzing network traffic.
- Netcat: Network debugging and testing.
Module 1: Introduction to Software Engineering
- Overview of SDE Intern Role:
- Key responsibilities and expectations.
- Understanding project impact and technical feedback.
- Collaboration with engineers and stakeholders.
- Software Development Lifecycle:
- Phases: Requirements, Design, Implementation, Testing, Deployment, Maintenance.
- Agile, Scrum, and other methodologies.
Module 2: Programming Fundamentals
- Java:
- Basics: Syntax, data types, control structures.
- Object-Oriented Programming (OOP): Classes, inheritance, polymorphism.
- Advanced topics: Streams, concurrency.
- Python:
- Basics: Syntax, data structures, functions.
- Libraries: NumPy, Pandas, Flask.
- Advanced topics: Decorators, generators.
- C++:
- Basics: Syntax, pointers, classes.
- OOP principles: Encapsulation, inheritance, polymorphism.
- Advanced topics: Templates, STL.
- JavaScript:
- Basics: Syntax, data types, control structures.
- Web Development: DOM manipulation, AJAX.
- Advanced topics: Asynchronous programming, frameworks (React, Node.js).
- Go:
- Basics: Syntax, types, functions.
- Concurrency: Goroutines, channels.
- Advanced topics: Interfaces, error handling.
Module 3: Data Structures and Algorithms
- Data Structures:
- Arrays, Linked Lists, Stacks, Queues, Hash Tables.
- Trees: Binary Trees, Binary Search Trees, Heaps.
- Graphs: Representation, traversal algorithms.
- Algorithms:
- Sorting: Quick Sort, Merge Sort, Heap Sort.
- Searching: Binary Search, Depth-First Search (DFS), Breadth-First Search (BFS).
- Dynamic Programming, Greedy Algorithms.
Module 4: Computer Science Fundamentals
Objectives:
- Build a solid understanding of key computer science concepts.
Content:
- Operating Systems:
- Processes and Threads, Memory Management, File Systems.
- Concurrency and Synchronization.
- Networking:
- Networking Basics: IP, DNS, HTTP/HTTPS.
- Protocols and Tools: TCP/IP, Sockets, Wireshark.
- Databases:
- Relational Databases: SQL, Schema Design, Transactions.
- NoSQL Databases: Key-Value Stores, Document Stores.
Module 5: System Design and Architecture
- System Design Basics:
- Principles: Scalability, Reliability, Maintainability.
- Components: Load Balancers, Databases, Caching.
- Architecture Patterns:
- Microservices, Monolithic Architecture, Serverless.
- Design Problems:
- Real-world scenarios: Design a URL shortening service, a messaging platform.
Module 1: Introduction to Machine Learning
- Introduction to ML Concepts:
- Types of learning: Supervised, Unsupervised, Reinforcement.
- Real-world applications of ML.
- Programming language for ML:
- Python programming language for ML.
- ML libraries: TensorFlow, Keras, PyTorch, Scikit-learn.
Module 2: Programming for Machine Learning
- Programming Languages:
- Python: Core syntax, data structures, functions.
- R: Basics of R, using R for statistical computing.
- ML Libraries and Tools:
- Scikit-learn, TensorFlow, Keras, PyTorch: How to implement different ML models.
- Jupyter Notebooks: Setting up an environment for ML projects.
- Git and Version Control: Best practices for managing code and collaborating with others.
Module 3: Mathematics and Statistics for Machine Learning
- Linear Algebra:
- Vectors, matrices, eigenvalues, matrix operations.
- Probability and Statistics:
- Bayes theorem, probability distributions, hypothesis testing.
- Calculus:
- Derivatives, integrals, gradient descent optimization.
Module 4: Core Machine Learning Algorithms
- Supervised Learning:
- Linear Regression, Logistic Regression.
- Support Vector Machines (SVM), Decision Trees, Random Forests.
- Unsupervised Learning:
- K-Means Clustering, Principal Component Analysis (PCA).
- Anomaly Detection, Dimensionality Reduction.
- Deep Learning:
- Neural Networks, CNNs, RNNs, Autoencoders.
Module 5: Data Handling and Preprocessing
- Data Wrangling:
- Using Pandas for data manipulation.
- Handling missing values, data normalization.
- Data Visualization:
- Visualizing data with Matplotlib and Seaborn.
- Understanding data through visualization techniques.
- SQL and Databases:
- Querying large datasets with SQL.
- Database integration with Python.
Module 6: Cloud Platforms and ML Services
- Cloud Platforms:
- Overview of AWS, Google Cloud, and Azure for ML projects.
- Setting up cloud environments.
- MLaaS:
- Introduction to services like Amazon SageMaker, Google AI Platform.
- Training and deploying models using MLaaS.
Module 7: Model Deployment and MLOps
- Containerization and Orchestration:
- Introduction to Docker and Kubernetes.
- Creating Docker containers for ML applications.
- MLOps:
- CI/CD pipelines for machine learning.
- Automating model training, testing, and deployment.
Module 1: Introduction to Fullstack Web Development
- Overview of Web Development
- Frontend vs Backend Development
- Fullstack Development Concepts
- Client-Server Architecture
- Tools and Frameworks
- Overview of HTML, CSS, JavaScript
- JavaScript Frameworks (React, Angular, Vue.js)
- Backend Technologies: Node.js, Ruby on Rails, Python (Django), PHP
Module 2: Frontend Development
- HTML & CSS Fundamentals
- Building the Structure: HTML5 Essentials
- Styling with CSS3 and Flexbox/Grid Layout
- Media Queries for Responsive Design
- Bootstrap and Material-UI Frameworks
- JavaScript for the Web
- JavaScript ES6+ Features (Arrow Functions, Promises, Async/Await)
- DOM Manipulation and Event Handling
- Creating Interactive Components
- Frontend Frameworks
- React Basics: Components, State, and Props
- Angular Basics: Modules, Services, and Routing
- Vue.js Overview
- UI/UX Design Principles
- Creating User-Friendly Interfaces
- Implementing Responsive and Adaptive Design
Module 3: Backend Development
- Introduction to Server-Side Programming
- Overview of Backend Technologies: Node.js, Python (Django), Ruby on Rails
- MVC Architecture (Model-View-Controller)
- RESTful APIs & CRUD Operations
- Designing REST APIs
- CRUD Operations (Create, Read, Update, Delete)
- Authentication & Authorization using JWT or OAuth
- Database Management
- SQL Databases: MySQL, PostgreSQL
- NoSQL Databases: MongoDB, Firebase
- Database Design and Normalization
- Writing Efficient Queries and Optimizing Performance
Module 4: Fullstack Integration
- Connecting Frontend and Backend
- Fetching Data using Axios or Fetch API
- State Management in React (Context API/Redux)
- Handling Forms and Validation
- Authentication & Authorization
- User Authentication with Passport.js, JWT, Firebase Authentication
- Role-Based Access Control
Module 5: Version Control & Collaboration
- Version Control with Git
- Git Basics: Branching, Merging, Rebasing
- GitHub/Bitbucket: Pull Requests, Code Reviews
- Collaboration in Agile Development: Sprints, Stand-Ups, Retrospectives
Module 6: Testing & Quality Assurance
- Testing Frontend and Backend
- Unit Testing with Jest/Mocha/Chai
- Integration and End-to-End Testing (Cypress, Selenium)
- Debugging and Error Handling Techniques
Module 7: Deployment & Cloud Platforms
- Deploying Web Applications
- Hosting on AWS, Heroku, or Google Cloud
- CI/CD Pipelines using Jenkins, GitHub Actions
- Application Monitoring and Maintenance
- Monitoring Tools (New Relic, LogRocket)
- Debugging Production Issues
Our ALumani Working in
Testimonial
Placements section of previous students
Pre-Internship Preparation Tracks
- Overview: This track focuses on building a strong foundation in machine learning concepts, tools, and applications. It prepares students for roles like Data Scientist, ML Engineer, and AI Researcher.
- Key Skills: Python programming, statistics, probability, machine learning algorithms (supervised/unsupervised learning), model evaluation, and deployment.
- Tools & Frameworks: TensorFlow, PyTorch, Scikit-learn, NumPy, Pandas.
- Interview Preparation: Focus on ML fundamentals, Python coding, model optimization, and real-world application of algorithms.
- Overview: This track covers the essentials of software engineering, programming, and problem-solving skills. It is designed for students aiming for roles like Software Developer and Full-Stack Engineer.
- Key Skills: Proficiency in programming languages (Java, Python, C++), data structures and algorithms, object-oriented design, and database management.
- Tools & Frameworks: Git, GitHub, Docker, IDEs like IntelliJ or VSCode.
- Projects: Build scalable applications, REST APIs, or solve coding challenges with real-world use cases.
- Interview Preparation: Focus on coding challenges, algorithmic problem-solving, system design, and object-oriented programming concepts.
- Overview: This track prepares students for front-end, back-end, and full-stack development roles. It includes learning the core technologies needed to build responsive and functional web applications.
- Key Skills: HTML, CSS, JavaScript, frameworks like React or Vue for frontend, Node.js, Django, or Flask for backend.
- Tools & Frameworks: React, Angular, Express, Django, MongoDB, MySQL.
- Projects: Build dynamic websites, e-commerce platforms, and full-stack web apps with RESTful APIs.
- Interview Preparation: Expect questions around web technologies, debugging, code optimization, and responsive design.
- Overview: This track equips students with the skills to manage and maintain large-scale systems with reliability and efficiency, preparing for SRE and DevOps roles.
- Key Skills: Systems architecture, automation, monitoring, incident management, and cloud infrastructure.
- Tools & Frameworks: Prometheus, Grafana, Jenkins, Kubernetes, AWS/GCP.
- Projects: Build a scalable, reliable infrastructure with auto-scaling, monitoring, and logging for a web application.
- Interview Preparation: Questions focus on automation, system failures, monitoring, scalability, and troubleshooting.
And here’s
a message for you
Here's what Industry says about Mr. Vimal Daga
- Esteemed Technology Leader: Known for his contributions to technology and innovation.
- Passion for Empowerment: Committed to sharing knowledge and helping individuals unlock their potential.
- Two-Decades Experience: Mastered diverse technologies, including cloud computing, AI, machine learning, DevOps, Cybersecurity & lot more
- Expert Educator: Focuses on making complex tech education simple for all, especially underprivileged students.
- Visionary Mentor: Advocates for holistic growth, including both technical skills and personal development.
- Global Workshops: Delivered numerous workshops and training sessions worldwide.
- Values-Driven Leadership: Emphasizes empathy, curiosity, and perseverance in his teachings.
- Awards & Recognitions: Recognized for driving innovation and collaboration across industries.
Inspiration in Tech: Continues to empower the next generation of innovators and change-makers.
Know Your Mentor
None of the technologies is complex since created by human beings. Hence, anyone can learn it and create something new.
#13 proudly presents Vimal Daga as the mentor for this program
A world record holder, Mr. Vimal Daga is a Technologist, Philanthropist & A TEDx Speaker who is dedicatedly working atowards his vision- “Awakening the youth through a culture of right education”.
He is the first one in the world to become “RedHat Certified Architect Level 25 along with Enterprise Application Level 10”. Companies benefited from his 19+ years of experience.
