
Business and customer service
Business & Customer Service
- Google Analytics and Tools
Master modern web analytics with Google Analytics 4 (GA4). This hands-on course covers everything from setting up GA4 and tracking key events to integrating with tools like Google Tag Manager, Search Console, Ads, and Looker Studio. You'll learn to create actionable reports, ensure data privacy compliance, and prepare for GA4 certification.
Learning Outcomes
- GA4 setup, events, conversions, and user tracking
- Custom dashboards and advanced reporting
- Integration with Google marketing tools
- Data privacy best practices (GDPR, Consent Mode)
- Real-world analytics project + certification prep
Course Structure Overview
Module | Topic | Key Highlights |
Introduction to Digital Analytics | Basics of Web Analytics | Importance in Marketing & UX, Key Metrics (Sessions, Bounce Rate, etc.), GA4 vs Universal Analytics |
GA4 Fundamentals | GA4 Structure & Reports | Account-Property-Data Stream, Event-Based Model, Default Reports (Acquisition, Engagement, etc.) |
Setting Up GA4 | GA4 Setup & Tools | Creating GA4 Property, Installing with GTM/Gtag.js, Debugging via DebugView |
Events & Conversions | Event Tracking | Built-in & Custom Events, Marking Conversions, Enhanced Measurement, Form/Button Tracking |
Audience & User Tracking | Understanding Users | User Properties, Segments, Cross-device/platform tracking |
Reporting & Insights | In-depth Analysis | Explorations (Funnel, Path), Lifecycle vs User Reports, Attribution Models, Exporting Reports |
Integration with Google Tools | Marketing Tool Connections | GTM (Tags, Triggers), GSC (SEO Reports), Google Ads (Conversions, Audiences), Looker Studio (Dashboards) |
Advanced Analytics | Deeper GA4 Features | Custom Dimensions/Metrics, User ID, BigQuery Integration, Predictive Metrics |
Privacy & Compliance | Legal & Ethical Use | GDPR/Data Retention, IP Anonymization, Consent Mode, Data Deletion |
Capstone Project | Hands-On & Certification | Full GA4 Setup, Stakeholder Reports, Certification Prep via Google Skillshop |
Assessment Methods
Module | Assessment Type | Activity |
1. Introduction | Quiz | Basic questions on analytics |
2. GA4 Basics | Quiz + Task | Explore GA4 reports |
3. GA4 Setup | Practical | Set up GA4 and test it |
4. Events & Conversions | Task | Track events (e.g., button click) |
5. User Tracking | Activity | Create segments, track users |
6. Reporting | Task | Build and export a report |
7. Tool Integration | Practical | Connect GA4 with other Google tools |
8. Advanced Analytics | Mini Project | Use advanced GA4 features |
9. Privacy | Quiz | Answer on GDPR and data settings |
10. Capstone Project | Final Project | Full GA4 setup + marketing report |
Course Schedule Overview
Module | Topic |
Module 1 | Introduction to Digital Analytics |
Module 2 | GA4 Fundamentals |
Module 3 | Setting Up GA4 |
Module 4 | Events & Conversions in GA4 |
Module 5 | Audience and User Tracking |
Module 6 | Reporting and Insights |
Module 7 | Integration with Google Tools |
Module 8 | Advanced Analytics |
Module 9 | Privacy and Compliance |
Module 10 | Capstone Project & Certification Prep |
Software Requirements: GA4, Google Tag Manager, Google Ads, Looker Studio, BigQuery, Google Skillshop
Instructor Name
Contact info
Certifications
- Sales, Marketing & Inventory Management
Build a powerful foundation in sales strategy, digital marketing, and inventory control. This practical course covers the full business cycle—from capturing leads and nurturing customers to managing inventory efficiently and driving growth. Ideal for entrepreneurs, marketers, and operations professionals.
Learning Outcomes
- B2B & B2C sales techniques and CRM tools
- Digital marketing: SEO, ads, email automation
- Inventory management: SKUs, stock control, JIT
- Linking sales and inventory systems for efficiency
- Campaign planning, performance metrics & reporting
Course Structure Overview
Module | Title | Key Highlights |
1 | Introduction to Sales & Marketing | Sales vs Marketing, Sales Funnel, Marketing Channels |
2 | Sales Techniques & CRM | B2B/B2C Strategies, CRM Tools (Salesforce, Zoho, HubSpot) |
3 | Digital Marketing Essentials | SEO, Email, Google Ads, Social Media, Google Analytics |
4 | Inventory Management Basics | Inventory Types, Costs, SKUs, FIFO/LIFO, Inventory Tools |
5 | Sales & Inventory Integration | Linking Sales & Inventory, Reordering, Stock Audits |
6 | Marketing Strategy & Campaigns | Marketing Plans, Targeting, Branding, ROI, Case Studies |
7 | Reporting & Metrics | KPIs for Sales, Marketing, Inventory, Dashboards |
8 | Capstone Project / Simulation | Business Simulation, Campaign Plan, Sales Report Presentation |
Assessment Methods
Module | Assessment Type | Activity |
Module 1 | Quiz | Basics of sales, marketing, and funnels |
Module 2 | Task + Quiz | Compare B2B/B2C, CRM tool demo (e.g., HubSpot) |
Module 3 | Hands-on Task | Create a sample ad or email campaign |
Module 4 | Practical Task | Classify inventory, calculate costs (FIFO, LIFO) |
Module 5 | Case Scenario | Link sales orders to inventory, suggest reorder plan |
Module 6 | Campaign Plan | Draft a basic marketing campaign with target audience |
Module 7 | Report Task | Prepare a sample KPI dashboard |
Module 8 | Final Project | Simulate business: sales strategy, marketing plan, inventory forecast & presentation |
Course Schedule
Module | Hours | Focus |
Module 1 | 4 hrs | Basics of sales, marketing, and customer journey |
Module 2 | 6 hrs | Sales strategies & CRM tools |
Module 3 | 6 hrs | Digital marketing channels & tools |
Module 4 | 6 hrs | Inventory types, costs, and systems |
Module 5 | 4 hrs | Sales-inventory integration & stock management |
Module 6 | 6 hrs | Marketing strategy, targeting & campaigns |
Module 7 | 4 hrs | Key performance metrics & dashboards |
Module 8 | 4 hrs | Capstone project & final presentations |
Software Requirements
CRM tools (HubSpot, Zoho, Salesforce)
- Google Sheets or Excel
- Google Analytics
- Google Ads / Facebook Ads
- Email Marketing Tools (Mailchimp, Zoho)
- SEO Tools (Google Search Console)
- Inventory Software (Zoho Inventory, QuickBooks)
- Presentation Tools (PowerPoint, Google Slides)
- Google Chrome
- Screen Recorder (Loom, OBS)
Instructor Name
Contact info
Certifications
- Customer Support and Service
Deliver outstanding customer experiences across every channel. This course trains professionals in effective communication, problem-solving, technical support, and service ethics. Gain the skills to handle customer issues with confidence and empathy—whether by phone, email, or live chat.
Learning Outcomes
- Communication and conflict resolution skills
- Support tools: CRM, helpdesks, ticketing systems
- Technical support and troubleshooting basics
- Quality assurance, feedback analysis, and service ethics
- Real-world support simulations and team collaboration
Course Structure Overview
Module | Title | Key Highlights |
1 | Introduction to Customer Service | Importance, Types of Support |
2 | Communication Skills | Listening, Empathy, Chat & Email Etiquette |
3 | Handling Customers | Difficult Customers, Personalization, Culture |
4 | Support Channels & Tools | Phone, Email, Chat, CRM Tools (Zendesk, etc.) |
5 | Problem Solving & Conflict | Root Cause, Escalation, Apologies |
6 | Technical Support Basics | Troubleshooting, Remote Support, Docs |
7 | Customer Feedback & Quality | Surveys, CSAT, QA Monitoring |
8 | Service Ethics & Professionalism | Ethics, Privacy, Code of Conduct |
9 | Teamwork & Stress Management | Burnout, Time Management, Queues |
10 | Capstone / Simulation | Live Support Scenarios, Role Play |
Assessment Methods
Module | Assessment Type | Activity |
Module 1 | Quiz | Basics of customer service types and importance |
Module 2 | Role Play | Email/chat etiquette and empathy practice |
Module 3 | Case Study | Handling difficult customer scenario |
Module 4 | Tool Demo | Use of helpdesk or CRM (Zendesk/Freshdesk) |
Module 5 | Written Task | Conflict resolution steps and escalation |
Module 6 | Simulation | Troubleshooting mock technical issue |
Module 7 | Survey Design | Create a sample feedback form (CSAT/NPS) |
Module 8 | Quiz | Ethics, privacy, and professionalism |
Module 9 | Reflection Task | Stress management and teamwork strategies |
Module 10 | Final Capstone | Live role play + peer/instructor evaluation |
Course Schedule
Module | Hours | Focus |
Module 1 | 3 hrs | Basics of customer service |
Module 2 | 5 hrs | Communication and listening skills |
Module 3 | 5 hrs | Dealing with customers effectively |
Module 4 | 5 hrs | Support channels and tools |
Module 5 | 5 hrs | Problem solving and resolution |
Module 6 | 4 hrs | Technical support foundations |
Module 7 | 4 hrs | Feedback and service quality |
Module 8 | 2 hrs | Service ethics and privacy |
Module 9 | 3 hrs | Stress and teamwork management |
Module 10 | 4 hrs | Final capstone simulation |
Software requirements :
- Zendesk
- Freshdesk
- Salesforce Service Cloud
- Zoho Desk
- Google Forms / SurveyMonkey (for feedback collection)
- TeamViewer / AnyDesk (remote support)
- Slack / Microsoft Teams (team collaboration)
- Google Sheets / Excel (reporting and tracking)
- Call/Chat Simulation Tools (or recorded scenarios)
Instructor Name
Contact info
Certifications
- Customer Data Management (CDM)
Unlock the power of customer data to drive smarter business decisions. This course guides you through the complete lifecycle of customer data—from collection and storage to segmentation, compliance, and analysis. Learn to manage data effectively using real-world tools and best practices.
Learning Outcomes
- Types of customer data and collection strategies
- Data storage systems: CRM, CDP, data lakes
- Cleaning, enriching, and segmenting customer data
- Privacy laws, data security, and governance
- Visualizing insights with dashboards and KPIs
Course structure Overview
Module | Title | Key Highlights |
1 | Introduction to Customer Data | Types of Data, CDM vs CRM vs CDP |
2 | Data Collection Strategies | Data Sources, Privacy, Data Types |
3 | Data Storage and Systems | CRMs, CDPs, Data Lakes/Warehouses |
4 | Data Quality and Hygiene | Cleaning, Accuracy, Tools |
5 | Customer Segmentation & Profiling | RFM, Personas, Targeting |
6 | Data Governance & Compliance | Privacy Laws, Consent, Ethics |
7 | Integrating & Syncing Systems | APIs, ETL, Middleware |
8 | Analytics and Reporting | KPIs, Dashboards, Predictive Models |
9 | Data Security & Risk Mitigation | Encryption, Breach Management |
10 | Capstone Project / Lab | Build CDM Mockup, Dashboard, Report |
Assessment Methods
Module | Assessment Type | Activity |
Module 1 | Quiz | Types of customer data & CDM vs CRM vs CDP |
Module 2 | Scenario Task | Design a privacy-compliant data collection plan |
Module 3 | Tool Demo | Compare features of CRM or CDP platforms |
Module 4 | Practical | Clean and validate a sample customer dataset |
Module 5 | Case Study | Segment a dataset and build buyer personas |
Module 6 | Quiz | Compliance laws (GDPR, CCPA, etc.) |
Module 7 | Project Task | Map and integrate tools using sample API flows |
Module 8 | Dashboard Task | Create a KPI dashboard using Looker Studio |
Module 9 | Written Task | Plan data breach response and security measures |
Module 10 | Final Capstone | Build full CDM mockup, segment data, present report |
Course Schedule
Module | Hours | Focus |
Module 1 | 3 hrs | Intro to customer data |
Module 2 | 4 hrs | Data collection methods |
Module 3 | 4 hrs | Data storage & systems |
Module 4 | 4 hrs | Data quality and cleaning |
Module 5 | 5 hrs | Segmentation and profiling |
Module 6 | 4 hrs | Governance & compliance |
Module 7 | 4 hrs | System integration & syncing |
Module 8 | 4 hrs | Analytics and reporting |
Module 9 | 3 hrs | Data security practices |
Module 10 | 3–5 hrs | Capstone project |
Software Requirements
- Salesforce
- Zoho CRM
- HubSpot
- Segment
- Tealium
- Adobe CDP
- BigQuery
- Snowflake
- OpenRefine
- Excel / Google Sheets
- io
- Looker Studio
- Power BI
- Zapier & Mulesoft
Instructor Name
Contact info
Certifications
- Product and Master Data Management
Master the principles of organizing, securing, and leveraging business-critical data. This course covers the end-to-end lifecycle of master and product data, equipping professionals with the skills to implement MDM and PIM systems that support business efficiency and compliance.
Learning Outcomes
- Fundamentals of Master Data and Product Information Management
- Data governance, quality frameworks, and architecture models
- Integration with systems like ERP, CRM, and e-commerce
- MDM tools, strategy development, and implementation best practices
- Privacy, security, and regulatory compliance (GDPR, CCPA)
Course structure Overview
Module | Title | Key Highlights |
1 | Introduction to Master Data Management (MDM) | What is Master Data, Types, MDM Lifecycle |
2 | Product Information Management (PIM) | PIM vs MDM, Product Attributes, Digital Commerce |
3 | Data Governance & Quality | Governance Frameworks, Ownership, Data Quality Tools |
4 | MDM Architecture & Integration | MDM Models, Data Modeling, ERP/CRM Integration |
5 | MDM Tools & Technologies | MDM/PIM Tools, Feature Comparison, Case Studies |
6 | MDM Strategy & Roadmap | Business Case, Stakeholders, Implementation Plan |
7 | Compliance & Security | GDPR/CCPA, Data Privacy, Audit Readiness |
8 | Capstone Project / Final Assessment | Full Strategy Proposal: Governance + Architecture |
Assessment Methods
Module | Assessment Type | Activity |
Module 1 | Quiz | Identify types and lifecycle of master data |
Module 2 | Practical Task | Create a product data hierarchy |
Module 3 | Written Task | Design a data governance policy |
Module 4 | Diagram Exercise | Draw MDM architecture and integration flow |
Module 5 | Tool Review | Compare features of MDM/PIM platforms |
Module 6 | Case Study | Build a basic MDM strategy roadmap |
Module 7 | Quiz | Identify compliance risks and data protection steps |
Module 8 | Capstone Project | Propose end-to-end MDM strategy with tools & governance |
Course Schedule
Module | Focus | Duration |
Module 1 | Introduction to MDM | 1 Week |
Module 2 | PIM (Product Information Management) Concepts | 1 Week |
Module 3 | Governance & Data Quality | 1 Week |
Module 4 | Architecture & System Integration | 1.5 Weeks |
Module 5 | Tools & Technologies | 1 Week |
Module 6 | Strategy & Roadmap | 1 Week |
Module 7 | Compliance & Security | 1 Week |
Module 8 | Capstone Presentation | 0.5 Week |
Software Requirements
- Informatica MDM
- SAP Master Data Governance
- Oracle Product Hub
- Stibo Systems
- Riversand MDM
- Salsify / Akeneo (for PIM)
- Microsoft Excel / Google Sheets
- Power BI / Looker Studio (for reporting)
- ERP/CRM Integrations (SAP, Salesforce, etc.)
Instructor Name
Contact info
Certifications
- Project Management
Turn plans into results with proven project-management frameworks. This course walks you through the entire project lifecycle—from initiation and planning to execution, control, and closure—while equipping you with the latest tools and methodologies to keep scope, schedule, and budget on track.
Learning Outcomes
- Build business cases, charters, and success criteria
- Create WBS, Gantt charts, budgets, and risk plans
- Lead teams, manage vendors, and assure quality
- Track progress with KPIs, Earned Value & change control
- Close projects, capture lessons, and archive assets
- Use MS Project, Asana, Jira, Trello; apply Waterfall, Agile & Scrum
- Navigate ethics, global collaboration, and AI-driven PM trends
Course structure Overview
Module | Title | Key Highlights |
1 | Introduction to Project Management | Definition, Lifecycle, Phases, Stakeholders |
2 | Project Initiation | Business Case, Charter, Stakeholders, Objectives |
3 | Project Planning | Scope, Schedule, Budget, Risk, Communication Plans |
4 | Project Execution | Teamwork, Quality, Resource & Vendor Management |
5 | Monitoring & Controlling | Performance Tracking, EVM, Change Control |
6 | Project Closure | Final Deliverables, Reviews, Documentation |
7 | Tools & Techniques | MS Project, Jira, Agile, Scrum, Dashboards |
8 | Ethics & Trends | PMI Ethics, Global PM, AI & Hybrid Trends |
Assessment Methods
Module | Assessment Type | Activity |
Module 1 | Quiz | Identify PM lifecycle & key roles |
Module 2 | Task | Draft a sample project charter |
Module 3 | Practical | Create WBS, schedule, and risk plan |
Module 4 | Scenario | Team leadership and quality management case |
Module 5 | Task | Calculate EVM & manage change |
Module 6 | Report | Closure checklist & lessons learned |
Module 7 | Tool Demo | Use Jira or Trello to manage a project |
Module 8 | Quiz | Ethics & current trends in PM |
Course Schedule
Week | Module | Focus Area |
Week 1 | Module 1 | PM Basics & Roles |
Week 2 | Module 2 | Project Initiation |
Week 3 | Module 3 | Planning Techniques |
Week 4 | Module 4 | Execution Strategies |
Week 5 | Module 5 | Monitoring Tools |
Week 6 | Module 6 | Closing Projects |
Week 7 | Module 7 | Tools & Methods |
Week 8 | Module 8 | Ethics & Trends |
Software Requirements:
- Microsoft Project
- Jira
- Trello
- Asana
- ClickUp
- Google Workspace / Microsoft 365
- Miro / Lucidchart (for visual planning)
- Slack / MS Teams (for communication)
Instructor Name
Contact info
Certifications
- Informatica & Cloud Data Management
Design, integrate, and govern data pipelines in the cloud with Informatica Intelligent Cloud Services (IICS). This course covers modern ETL/ELT practices, data quality, and governance for cloud warehouses such as Snowflake, Redshift, BigQuery, and Azure Synapse.
Learning Outcomes
- Set up Informatica IICS and build cloud data pipelines
- Extract, transform, and load structured & semi-structured data
- Apply data quality, lineage, and governance best practices
- Optimize performance and ensure security & compliance (GDPR, HIPAA) Implement real-time vs. batch ingestion into cloud data warehouses
Course structure Overview
Module | Title | Key Highlights |
1 | Introduction to Data Management & Cloud Computing | Data principles, Cloud models (IaaS, PaaS, SaaS), Cloud data integration |
2 | Informatica Overview | Informatica tools: Power Center, IICS, MDM, Data Quality |
3 | Cloud Data Integration (IICS) | IICS setup, pipeline design, source/target config |
4 | Data Transformation & ETL | ETL process, Mapping logic, Performance tuning |
5 | Data Quality & Governance | Profiling, Cleansing, Validation, Metadata management |
6 | Cloud Data Warehousing | Integration with Snowflake, BigQuery, Redshift, ELT vs ETL |
7 | Security, Compliance & Monitoring | Encryption, Access control, GDPR, Monitoring tools |
8 | Project & Use Cases | Build a data pipeline, Apply quality tools, Present case study |
Assessment Methods
Assessment Type | Details |
Quizzes | After each module to test understanding |
Practical Assignments | ETL design, data quality tasks, IICS pipeline setup |
Capstone Project | Build & present a complete cloud data pipeline |
Participation & Labs | Hands-on activities, discussion involvement |
Final Evaluation | Based on project presentation and accuracy |
Course Schedule
Module | Duration |
|
Module 1 | 4 hours | Intro to Data & Cloud |
Module 2 | 4 hours | Informatica Overview |
Module 3 | 6 hours | IICS Data Integration |
Module 4 | 6 hours | ETL & Transformation |
Module 5 | 5 hours | Data Quality & Governance |
Module 6 | 5 hours | Cloud Warehousing |
Module 7 | 4 hours | Security & Compliance |
Module 8 | 6 hours | Final Project |
Software Requirements
- Informatica Intelligent Cloud Services (IICS)
- Informatica PowerCenter (basic overview)
- Informatica Data Quality (IDQ)
- Snowflake / Google BigQuery / AWS Redshift
- Cloud Platform Account (AWS/GCP/Azure)
- Microsoft Excel or Google Sheets (for profiling)
- Remote Desktop / VM (optional for practice labs)
Instructor Name
Contact info
Certifications
Development & Training
- Java Full Stack Development
Master full stack web development using Java, Spring Boot, and modern frontend frameworks like React or Angular. This course takes you from UI design to backend API development and deployment on the cloud.
Learning Outcomes:
- Build responsive UIs with HTML, CSS, JavaScript, and React/Angular
- Develop robust REST APIs using Java, Spring Boot, and Spring Security
- Work with MySQL, PostgreSQL, and MongoDB databases
- Use Git, Postman, Docker, and CI/CD tools
- Deploy full stack apps to AWS, Heroku, or Render
Course structure Overview
Module | Topics | Key Highlights |
Frontend Development | HTML5, CSS3, JavaScript | Responsive design, Flexbox, DOM, ES6 |
React.js or Angular | Components, props/state, forms, hooks/services | |
UI Tools (Optional) | Bootstrap, Tailwind CSS, Figma basics | |
Backend Development with Java | Core Java | OOP, Collections, Exceptions, Streams, Lambda |
Spring & Spring Boot | REST APIs, JPA, Annotations, Validations | |
Security | Spring Security, Basic auth & authorization | |
Database Management | Relational(MySQL/PostgreSQL) | SQL CRUD, Joins, Constraints |
NoSQL (MongoDB) | CRUD operations, Schema design | |
Tools & DevOps Basics | Version Control | Git and GitHub |
Build & Testing Tools | Maven/Gradle, JUnit, Postman | |
DevOps | Docker basics, CI/CD (Jenkins/GitHub Actions) | |
Deployment & Cloud | Hosting Platforms | Heroku, AWS EC2, Render |
Config Management | Environment variables, Backend-Frontend integration | |
6. Capstone Project | Full Stack Project | React/Angular frontend + Spring Boot backend + DB + Deployment |
Assessment Methods
Module | Assessment | What to Do |
1. Frontend | Small Project + Quiz | Build a responsive web page with HTML, CSS, JS. Take a short quiz on basics. |
2. Backend | Coding Task + Quiz | Write Java programs and create a simple REST API. Quiz on Java and Spring. |
3. Database | Practical + Quiz | Write SQL queries and do basic MongoDB tasks. Quiz on databases. |
4. Tools & DevOps | Hands-on Task + Quiz | Use GitHub, build tools, test APIs, and create a Dockerfile. Quiz on tools. |
5. Deployment | Deployment Task | Deploy your app on Heroku or AWS. |
6. Final Project | Full Project + Presentation | Build a full app with frontend, backend, database, and deploy it. Present your work. |
Course Schedule
Module | Topics Covered | Duration |
Module 1 | Frontend Development: HTML, CSS, JS, Responsive Design, React/Angular, Bootstrap | 3 Weeks |
Module 2 | Backend with Java: Core Java, Spring Boot, REST APIs, Spring Security | 3 Weeks |
Module 3 | Database Management: MySQL/PostgreSQL, MongoDB Basics | 2 Weeks |
Module 4 | Tools & DevOps: Git, GitHub, Maven, JUnit, Postman, Docker, CI/CD Basics | 2 Weeks |
Module 5 | Deployment & Cloud: Deploy to Heroku/AWS, Connect Frontend & Backend | 1.5 Weeks |
Module 6 | Capstone Project: Build & Deploy Full-Stack App | 2 Weeks |
Software Requirements:
- Code Editor (VS Code or Sublime Text)
- Web Browser (Chrome or Firefox) Java JDK (version 11 or above)
- Java IDE (IntelliJ IDEA or Eclipse)
- Spring Boot
- Database (MySQL or PostgreSQL)
- MongoDB
- Git and GitHub
- Build Tool (Maven or Gradle)
- Testing Tools (JUnit, Postman)
- Docker
- Heroku or AWS CLI (for deployment)
Instructor Name
Contact info
Certifications
- Go Full Stack Development (Go FSD)
Become a skilled full stack developer with Go (Golang) and modern frontend frameworks like React or Vue.js. Learn to build scalable web applications from frontend design to backend APIs, plus deployment.
Learning Outcomes
- Develop responsive frontends using HTML, CSS, JavaScript, and React or Vue
- Build powerful Go backend REST APIs with concurrency and middleware
- Work with PostgreSQL, MySQL, and MongoDB databases
- Master Git workflows, unit testing, Docker containers, and CI/CD pipelines
- Deploy full stack apps on popular cloud platforms like AWS, Heroku, and Render
Course structure Overview
Module | Topics | Key Highlights |
Frontend Development | · HTML5, CSS3, JavaScript · (ES6+) · React.js or Vue.js · Framework · UI Frameworks |
Responsive layouts (Flexbox, Grid), DOM & events Components, props/state, routing, forms, API calls Bootstrap, Tailwind CSS, Material UI (optional) |
Backend Development with Go | · Go Basics · Web APIs with Go · Microservices (Optional) |
Syntax, types, pointers, structs, interfaces, concurrency REST APIs (net/http, mux, Gin), middleware, JSON, sessions gRPC, Protocol Buffers, service design |
Database Management | · Relational DB · NoSQL DB | SQL with PostgreSQL or MySQL, Go integration (gorm) MongoDB basics and Go integration |
DevOps and Tools | · Version Control · Testing & Debugging · Build & Dependency · Docker & Containers · CI/CD |
Git & GitHub workflows Go unit tests, Postman API testing Go modules, build and compile projects Dockerfiles, Docker Compose multi-container setup GitHub Actions for automation |
Deployment | · Hosting Platforms · Config & Security | Deploy on Render, Railway, Heroku, AWS EC2 Environment variables, CORS, reverse proxy |
6. Capstone Project | · Full Stack Project | Go backend + React/Vue frontend, auth, CRUD, API, DB, deploy with Docker |
Assessment Methods
Module | Assignment | What to Do |
Frontend | Build a webpage React or Vue mini app | Make a responsive page with HTML, CSS, and JS Create a small app with components and API calls |
Backend | Go basics coding Build a REST API | Practice Go functions, structs, and concurrency Create a simple API with Go (CRUD operations) |
Database | SQL & MongoDB | Write SQL queries and do MongoDB operations using Go |
DevOps | GitHub & Testing Docker setup | Push code to GitHub and test APIs with Postman Write Dockerfiles and use Docker Compose |
Deployment | Deploy app | Deploy your app on Heroku, Render, or Railway |
Final Project | Full-stack app | Build and deploy a full web app with frontend, backend, and database |
Course Schedule
Module | Topics Covered | Duration |
Frontend | HTML, CSS, JavaScript, React or Vue | 2.5 – 3 Weeks |
Backend (Go) | Go basics, REST API, Authentication | 2.5 – 3 Weeks |
Database | MySQL/PostgreSQL, MongoDB | 1.5 – 2 Weeks |
DevOps Tools | Git, Postman, Docker, GitHub Actions | 1.5 Weeks |
Deployment | Hosting on Render, Heroku, or AWS | 1 Week |
Capstone Project | Build and deploy a full web app | 2 Weeks |
Software Requirements
- Visual Studio Code
- Google Chrome or Firefox
- Go (Golang)
- PostgreSQL / MySQL
- MongoDB
- Postman
- Git
- GitHub
- Docker
- Heroku CLI / Railway / Render (for deployment)
Instructor Name
Contact info
Certifications
- UX/UI Design and Development
Master the art and science of user-centered digital design and development. This course covers everything from research and wireframing to frontend coding and usability testing.
Key Modules
- Understand UX vs UI and design thinking principles
- Conduct user research and create personas
- Design wireframes, prototypes, and high-fidelity mockups using tools like Figma and Adobe XD
- Learn visual design fundamentals and accessibility best practices
- Develop responsive interfaces with HTML, CSS, JavaScript, and frameworks like React or Vue
- Perform usability testing, heuristic evaluations, and iterate designs
- Collaborate effectively with developers using design systems and version control
Course structure Overview
Module | Topic | Key Highlights |
1. Introduction to UX/UI Design | · Definitions: UX vs. UI · Importance of user-centered design · Design thinking process · UX principles and heuristics | Understand core concepts and differences between UX and UI; apply user-centered approaches and design thinking. |
2. User Research and Analysis | · Conducting user interviews and surveys · Creating user personas · Customer journey mapping · Competitive analysis | Learn methods for gathering user insights and analyzing user behavior. |
3. UX Design Fundamentals | · Information architecture · User flows and task analysis · Wireframing (low-fidelity) · Prototyping tools overview | Create structural design of user experiences and understand lowfidelity design process. |
4. UI Design Principles | · Visual design fundamentals (color, typography, spacing) · Layouts and design grids · Design systems and component libraries · Accessibility and inclusive design | Apply design principles and create visually appealing and accessible UI components. |
5. Design Tools and Prototyping | · Tools: Figma, Adobe XD, Sketch · Creating high-fidelity mockups · Interactive prototypes · Handoff for development (design specs and assets) | Use industry-standard tools to build detailed designs and communicate them effectively to developers. |
6. Frontend Development for Designers | · HTML5, CSS3, JavaScript basics Responsive design and media queries CSS frameworks (Bootstrap or Tailwind CSS) · Integrating design into web projects | Bridge the gap between design and development through practical coding skills. |
7. UI Development with Frameworks | · Introduction to React.js or Vue.js · Components, props, and state · Connecting UI to APIs · Form handling and validation | Build dynamic interfaces using modern JavaScript frameworks and connect to backend services. |
8. UX Testing and Iteration | · Usability testing methods (moderated, unmoderated) · Heuristic evaluation · A/B testing basics · Using analytics for UX improvements | Evaluate and refine UX through feedback and data-driven decision making. |
9. Design Systems and Collaboration | · Building reusable UI components · Using and managing a design system Working with developers (Zeplin, Figma inspect, Storybook) · Version control and design handoff | Collaborate efficiently with teams and maintain consistency using design systems and tools. |
10. Capstone Project | · Design and develop a complete web or mobile app UI/UX · Conduct research, create wireframes and prototypes · Develop frontend and test usability · Present final product with rationale documentation | Apply all learned skills in a comprehensive real-world project, showcasing end-to-end UX/UI design And development |
Assessment Methods
Module | Assessment Type | Description / Purpose |
1. Introduction to UX/UI Design | Quiz / Short Test | Test knowledge of UX vs. UI, design thinking, and UX principles. |
2. User Research and Analysis | Practical Assignment | Conduct interviews, create user personas, and map customer journeys. |
3. UX Design Fundamentals | Wireframe Submission | Submit low-fidelity wireframes and user flow diagrams. |
4. UI Design Principles | Design Exercise | Create a visual mockup applying color, typography, layout, and accessibility. |
5. Design Tools and Prototyping | Tool-based Project | Build a high-fidelity prototype using Figma/Adobe XD/Sketch. |
6. Frontend Development for Designers | Coding Exercises | Simple HTML/CSS/JS tasks demonstrating responsive design. |
7. UI Development with Frameworks | Mini Project | Build a small React/Vue app component, connect it to a mock API. |
8. UX Testing and Iteration | Usability Testing Report | Conduct usability tests and submit a report with findings and suggestions. |
9. Design Systems and Collaboration | Collaborative Project / Peer Review | Create reusable UI components and document a design system. |
10. Capstone Project | Final Project Presentation and Report | Design, develop, test, and present a full app with documentation. |
Course Schedule
SI No. | Module | Topics Covered | Duration |
1 | Introduction to UX/UI Design | UX vs. UI, user-centered design, design thinking, UX principles | 1 Week |
2 | User Research and Analysis | Interviews, surveys, personas, journey mapping, competitor analysis | 1.5 Weeks |
3 | UX Design Fundamentals | Information architecture, user flows, wireframing, prototyping overview | 1.5 Weeks |
4 | UI Design Principles | Color, typography, layout, design systems, accessibility | 1.5 Weeks |
5 | Design Tools and Prototyping | Figma, Adobe XD, Sketch, high-fidelity mockups, prototypes, handoff | 1.5 – 2 Weeks |
6 | Frontend Development for Designers | HTML5, CSS3, JavaScript basics, responsive design, CSS frameworks | 2 Weeks |
7 | UI Development with Frameworks | React.js or Vue.js basics, components, props, state, API integration | 2.5 – 3 Weeks |
8 | UX Testing and Iteration | Usability testing, heuristic evaluation, A/B testing, analytics | 1 Week |
9 | Design Systems and Collaboration | Reusable components, design systems, developer collaboration | 1 Week |
10 | Capstone Project | End-to-end app design, development, testing, presentation | 2 Weeks |
Software Requirements
- Figma
- Adobe XD
- Sketch
- Zeplin
- Visual Studio Code
- Chrome / Firefox / Edge
- js & npm
- Bootstrap
- Tailwind CSS
- js
- js
Instructor Name
Contact info
Certifications
- MEAN Stack Development Course
Master full stack web development with the MEAN stack: MongoDB, Express.js, Angular, and Node.js. This course covers everything from database design and backend API creation to frontend development and deployment. Gain hands-on experience building scalable, secure web applications with user authentication and best industry practices.
Learning Outcomes:
- Build and integrate full stack applications using MEAN technologies
- Design NoSQL databases and RESTful APIs
- Develop dynamic frontends with Angular
- Implement JWT-based authentication and role-based access Deploy applications on cloud platforms
Course structure Overview
SI no. | Module | Key Highlights |
1 | Introduction to MEAN Stack | · Full stack development overview, MEAN components, advantages, environment setup |
2 | MongoDB (Database Layer) | · NoSQL basics, CRUD operations, schema design, Mongoose ODM, relationships and population |
3 | Express.js (Backend Framework) | · Express server setup, routing, middleware, RESTful APIs, request handling, · MongoDB integration |
4 | Node.js (Runtime Environment) | · Node.js architecture, async programming, filesystem, building REST APIs, npm packages |
5 | Angular (Frontend Framework) | · Angular basics, TypeScript, components, modules, services, data binding, routing, · HTTP client, forms |
6 | Integration of Frontend & Backend | · Connecting Angular with Express, HTTP methods, CORS, proxy config, authentication flow |
7 | User Authentication & Authorization | · JWT authentication, securing routes, role-based access, token storage |
8 | Deployment and DevOps Basics | · Preparing for production, hosting backend and database, hosting frontend, environment variables |
9 | Tools and Best Practices | · Postman, Git/GitHub, debugging tools, code structuring, modular development |
10 | Capstone Project | · Full stack MEAN app: frontend, backend, database, authentication, deployment |
Assessment Methods
Module | Assessment Type | Description / Purpose |
Introduction to MEAN Stack | Quiz | Test understanding of MEAN components, full stack concepts, setup |
MongoDB (Database Layer) | Practical Assignment | Perform CRUD operations and design schemas using MongoDB and Mongoose |
Express.js (Backend Framework) | Coding Exercise | Build RESTful APIs with routing, middleware, and error handling |
Node.js (Runtime Environment) | Coding Exercise | Implement asynchronous code and file handling in Node.js |
Angular (Frontend Framework) | Project Task | Create Angular components, routing, data binding, and forms |
Integration of Frontend & Backend | Mini Project | Connect Angular frontend with Express backend using APIs |
User Authentication Authorization | Practical Task | Implement JWT auth, secure routes, and role-based access |
Deployment and DevOps Basics | Deployment Exercise | Deploy MEAN app on cloud platforms and configure environment |
Tools and Best Practices | Documentation / Quiz | Use Postman, Git workflows, debugging, and best coding practices |
Capstone Project | Final Project Presentation | Complete MEAN stack application development and deployment |
Course Schedule
SI No. | Module | Focus / Activities | Duration |
1 | Introduction to MEAN Stack | Overview, environment setup, install tools | 1 Week |
2 | MongoDB (Database Layer) | Learn NoSQL, CRUD, schema design, Mongoose basics | 1.5 Weeks |
3 | Express.js (Backend Framework) | Setup Express server, routing, middleware, REST APIs | 1.5 Weeks |
4 | Node.js (Runtime Environment) | Node.js architecture, async programming, file system usage | 1.5 Weeks |
5 | Angular (Frontend Framework) | Components, modules, data binding, routing | 2 – 2.5 Weeks |
6 | Integration of Frontend & Backend | Connect frontend to backend, handle HTTP requests, CORS | 1 Week |
7 | User Authentication & Authorization | JWT, securing routes, role-based access | 1 Week |
8 | Deployment and DevOps Basics | Production build, deploy backend & frontend | 1 Week |
9 | Tools and Best Practices | Postman, Git/GitHub, debugging, code structuring | 0.5 – 1 Week |
10 | Capstone Project | Build and present full MEAN stack application | 2 Weeks |
Software Requirements
- js & npm
- MongoDB (Community Server or Atlas Cloud)
- Angular CLI
- Code Editor (Visual Studio Code recommended)
- Web Browsers (Chrome, Firefox, Edge)
- Postman (API testing)
- Git (version control)
- GitHub or GitLab (repository hosting)
- Heroku (or other hosting platform for backend deployment) Netlify / Vercel (for frontend deployment)
Instructor Name
Contact info
Certifications
5.MERN Stack Development Course
Learn to build powerful full stack web applications using the MERN stack: MongoDB, Express.js, React, and Node.js. This course guides you through backend API creation, frontend React development, secure user authentication, and deployment to cloud platforms.
Learning Outcomes
- Design and manage NoSQL databases with MongoDB and Mongoose
- Develop RESTful APIs using Express and Node.js
- Build dynamic, responsive frontends with React and hooks
- Integrate frontend and backend with secure JWT authentication
- Deploy full stack applications using Heroku, Netlify, and MongoDB Atlas
Course structure Overview
SI NO. | Module | Key Highlights |
1 | Introduction to MERN Stack | · Full stack overview, MERN components, advantages, environment setup |
2 | MongoDB (Database Layer) | · NoSQL vs SQL, data model, CRUD operations, Mongoose ODM |
3 | Express.js (Backend Framework) | · Express server setup, RESTful API design, routing, middleware, error handling |
4 | Node.js (Runtime Environment) | · Node.js architecture, npm, async programming, file handling, backend logic |
5 | React.js (Frontend Framework) | · React basics, JSX, functional components, hooks, props/state, routing, forms, API calls |
6 | Integration: Frontend & Backend | · API calls from React, async data handling, CORS, proxy, project structure |
7 | Authentication & Authorization | · User registration/login, bcrypt, JWT, token storage, route protection |
8 | Deployment and DevOps Basics | · Environment variables, React build, frontend & backend deployment, CI/CD basics |
9 | Tools and Best Practices | · Postman, Git/GitHub, debugging, linting, code formatting, folder structure |
10 | Capstone Project | · Build, deploy full MERN app with auth, roles, frontend & backend integration |
Assessment Methods
Module | Assessment Type | Description |
Introduction to MERN Stack | Quiz | Test understanding of MERN basics and environment setup |
MongoDB | Practical Assignment | CRUD operations and schema design using MongoDB and Mongoose |
Express.js | Coding Exercise | Create RESTful APIs with routing, middleware, and error handling |
Node.js | Coding Exercise | Implement async code, file handling, and backend logic |
React.js | Project Task | Build React components, routing, forms, and API integration |
Integration: Frontend & Backend | Mini Project | Connect React frontend with Express backend APIs |
Authentication & Authorization | Practical Task | Implement JWT authentication, token storage, and protected routes |
Deployment and DevOps Basics | Deployment Exercise | Deploy frontend and backend to cloud platforms |
Tools and Best Practices | Quiz / Documentation | Git workflows, debugging, linting, and modular code practices |
Capstone Project | Final Project Presentation | Full MERN stack app development and deployment |
Course Schedule
SI No. | Module | Focus / Activities |
1 | Introduction to MERN Stack | Overview, environment setup, install tools |
2 | MongoDB | NoSQL basics, CRUD, schema design, Mongoose |
3 | Express.js | Server setup, routing, REST APIs, middleware |
4 | Node.js | Architecture, async programming, backend logic |
5 | React.js | Components, hooks, routing, forms, API calls |
6 | Integration: Frontend & Backend | API calls, async handling, CORS, project structure |
7 | Authentication & Authorization | JWT auth, bcrypt, route protection |
8 | Deployment and DevOps Basics | Building app, environment variables, deploying |
9 | Tools and Best Practices | Postman, Git, debugging, linting, modular code |
10 | Capstone Project | Build and deploy full MERN stack application |
Software Requirements:
- js & npm
- MongoDB (Community Server or Atlas)
- Visual Studio Code (recommended)
- Web Browsers (Chrome, Firefox, Edge)
- Postman (API testing)
- Git (version control)
- GitHub or GitLab (repository hosting)
- Netlify or Vercel (frontend deployment)
- Heroku, Render, or similar (backend deployment)
- Optional: ESLint, Prettier (code formatting and linting tools)
Instructor Name
Contact info
Certifications
- Product Management Course
Equip yourself with the essential skills to lead successful products from concept to launch. This course covers the full product lifecycle, market research, strategy, agile planning, design collaboration, go-to-market tactics, and growth techniques.
Learning Outcomes
- Define and align product vision with business goals
- Conduct market and user research to validate ideas
- Build effective product roadmaps and manage agile workflows
- Collaborate with design and development teams
- Plan and execute product launches
- Analyze metrics to drive product growth and iteration
Course structure Overview
SI No. | Module | Key Highlights |
1 | Introduction to Product Management | · Role, responsibilities, lifecycle, stakeholders, and collaboration |
2 | Market and User Research | · Market trends, customer discovery, research methods, personas, journey maps |
3 | Product Strategy and Vision | · Vision, mission, business alignment, strategy, prioritization methods |
4 | Product Planning and Roadmapping | · SDLC, Agile, Lean, MVP, PRDs, backlog grooming, sprint planning |
5 | Agile and Scrum for Product Managers | · Agile principles, Scrum roles, user stories, sprint cycles |
6 | Product Design and Development | · Collaboration with designers, wireframes, prototypes, design tools |
7 | Go-to-Market Strategy | · Positioning, launch, sales enablement, customer onboarding |
8 | Metrics, Analytics, and Feedback | · KPIs, analytics tools, A/B testing, customer feedback loops |
9 | Product Growth and Iteration | · Growth hacking, PLG, feature adoption, scaling |
10 | Capstone Project | · Develop strategy, conduct research, define roadmap & KPIs, pitch |
Assessment Methods
Module | Assessment Type | Description |
Introduction to Product Management | Quiz | Test understanding of PM roles, lifecycle, and collaboration |
Market and User Research | Case Study / Assignment | Conduct market research and create user personas |
Product Strategy and Vision | Strategy Document | Write product vision and roadmap with prioritization |
Product Planning and Roadmapping | Practical Task | Create MVP plan, PRD, and sprint backlog |
Agile and Scrum for Product Managers | Role Play / Quiz | Demonstrate Scrum roles, user stories, and sprint planning |
Product Design and Development | Collaboration Exercise | Work on wireframes/prototypes using design tools |
Go-to-Market Strategy | Launch Plan | Develop product positioning and go-to-market plan |
Metrics, Analytics, and Feedback | Analytics Report | Define KPIs, analyze data, and interpret feedback |
Product Growth and Iteration | Growth Strategy Proposal | Propose growth tactics and scaling plans |
Capstone Project | Final Presentation | Pitch complete product strategy and roadmap |
Course Schedule
Week | Module | Focus / Activities | Duration |
1 | Introduction to Product Management | PM roles, lifecycle, stakeholders, collaboration | 1 Week |
2 | Market and User Research | Market analysis, customer discovery, research methods | 1 Week |
3 | Product Strategy and Vision | Vision, strategy creation, prioritization techniques | 1 Week |
4 | Product Planning and Roadmapping | Agile/Lean SDLC, MVP, PRDs, backlog grooming | 1 Week |
5 | Agile and Scrum for Product Managers | Scrum framework, roles, user stories, sprint planning | 1 Week |
6 | Product Design and Development | Wireframing, prototyping, design thinking, design tools | 1 Week |
7 | Go-to-Market Strategy | Positioning, launch planning, sales enablement | 1 Week |
8 | Metrics, Analytics, and Feedback | Defining KPIs, analytics tools, A/B testing | 1 Week |
9 | Product Growth and Iteration | Growth hacking, PLG strategies, feature adoption | 1 Week |
10 | Capstone Project | Full strategy development and pitch presentation | 1 Week |
Software Requirements:
- Figma
- Sketch
- Adobe XD
- Google Analytics
- Mixpanel
- Amplitude
- Jira
- Trello
- Asana
- Google Forms
- Typeform
- Slack
- Microsoft Teams
- PowerPoint and Google slides
Instructor Name
Contact info
Certifications
- Data Engineering Course
Build the skills to design, develop, and maintain robust data pipelines and architectures. This course covers programming, database design, big data tools, cloud platforms, real-time processing, and data governance.
Learning Outcomes
- Develop ETL/ELT pipelines and data workflows
- Work with relational and NoSQL databases
- Use big data technologies like Hadoop, Spark, and Kafka
- Deploy data solutions on AWS, Azure, or GCP
- Implement data quality, security, and compliance best practice
Course structure Overview
SI no. | Module | Key Highlights |
1 | Introduction to Data Engineering | · Role of Data Engineer, difference from Data Science, pipelines overview |
2 | Programming for Data Engineering | · Python, SQL, shell scripting, APIs, JSON/XML |
3 | Data Modeling and Databases | · Relational & NoSQL DBs, data warehousing, schemas, normalization |
4 | ETL & ELT Pipelines | · ETL vs ELT, building pipelines with Airflow/Luigi, ingestion, error handling |
5 | Big Data Technologies | · Hadoop ecosystem, Apache Spark, distributed processing |
6 | Cloud Platforms | · AWS/Azure/GCP services for data, pipeline deployment |
7 | Data Lakes and Warehousing | · Data lakes/lakehouses, Snowflake, storage formats, batch vs streaming |
8 | Real-Time Data Processing | · Kafka, producers/consumers, stream processing with Flink/Spark Streaming |
9 | Data Quality, Governance & Security | · Data validation, schema enforcement, RBAC, encryption, compliance |
10 | Monitoring and Orchestration | · Airflow workflows, monitoring, CI/CD, logging |
11 | Capstone Project | · End-to-end data pipeline: ingest, store, transform, model data |
Assessment Methods
Module | Assessment Type | Description |
Introduction to Data Engineering | Quiz | Basic concepts of data engineering roles and pipeline workflows |
Programming for Data Engineering | Coding Assignment | Python scripting and SQL queries |
Data Modeling and Databases | Case Study / Exercise | Design schemas and data modeling tasks |
ETL & ELT Pipelines | Practical Project | Build an ETL pipeline using Airflow or Luigi |
Big Data Technologies | Quiz + Lab | Concepts of Hadoop and Spark with practical exercises |
Cloud Platforms | Hands-on Assignment | Use AWS/Azure/GCP data services and deploy pipelines |
Data Lakes and Warehousing | Report / Practical | Work on data lake storage formats and data warehousing concepts |
Real-Time Data Processing | Lab / Demo | Kafka setup and stream processing task |
Data Quality, Governance & Security | Assignment | Data validation, security policies, and compliance checks |
Monitoring and Orchestration | Practical Task | Setup Airflow workflows and monitoring |
Capstone Project | Final Project Presentation | End-to-end data pipeline build and demo |
Course Schedule
Week | Module | Focus / Activities | Duration |
1 | Introduction to Data Engineering | Concepts, roles, pipeline overview | 1 Week |
2 | Programming for Data Engineering | Python scripting, SQL, APIs | 1 Week |
3 | Data Modeling and Databases | Schema design, relational & NoSQL databases | 1 Week |
4 | ETL & ELT Pipelines | Building ETL pipelines, error handling | 1 Week |
5 | Big Data Technologies | Hadoop, Spark fundamentals | 1 Week |
6 | Cloud Platforms | Using AWS/Azure/GCP for data workloads | 1 Week |
7 | Data Lakes and Warehousing | Data lakes concepts, storage formats | 1 Week |
8 | Real-Time Data Processing | Kafka setup, stream processing | 1 Week |
9 | Data Quality, Governance & Security | Validation, security, compliance | 1 Week |
10 | Monitoring and Orchestration | Airflow workflows, pipeline monitoring | 1 Week |
11 | Capstone Project | End-to-end pipeline design and demo | 1 Week |
Software Requirements:
- Python
- SQL (pgAdmin, MySQL Workbench)
- Apache Airflow
- Apache Spark Apache Kafka
- Hadoop (HDFS, MapReduce)
- MongoDB / Cassandra
- Cloud platforms (AWS, Azure, GCP)
- Shell scripting (Bash)
- Snowflake (or other data warehouses)
- Monitoring tools (Prometheus, Grafana)
Instructor Name
Contact info
Certifications
- Python Full Stack Development Course
Learn to build complete web applications using Python for backend and modern frontend technologies. This course covers frontend basics, Python programming, backend frameworks, databases, REST APIs, DevOps, and deployment.
Learning Outcomes
- Frontend development with HTML, CSS, JavaScript, and optional React/Vue.js
- Core Python programming and OOP principles
- Backend development using Flask or Django
- Relational databases with PostgreSQL/MySQL and ORM integration
- Building and consuming RESTful APIs with authentication
- Version control with Git, containerization with Docker, and CI/CD basics
- Deploying full stack apps to cloud platforms
Course structure Overview
Module | Topics | Key Highlights |
Frontend Development | · HTML5, CSS3, Responsive design, JS (ES6+), · Optional React/Vue.js basics | Build responsive user interfaces and interactive frontends |
Python Programming | · Variables, data types, OOP, modules, file handling, working with JSON and APIs | Core Python programming skills |
BackendDevelopment | · Flask or Django: routing, templates, forms, middleware, REST APIs | Develop server-side applications and APIs |
Database Management | · SQL basics, PostgreSQL/MySQL, ORM · (SQLAlchemy/Django ORM), optional MongoDB | Manage and interact with relational and NoSQL databases |
REST API and Web Services | · Building APIs, serialization, consuming APIs, · JWT/OAuth authentication | Create and consume RESTful services |
Version Control & DevOps | · Git/GitHub, branching, Docker basics, CI/CD overview | Manage code and automate deployment workflows |
Deployment | · Hosting backend (Heroku, AWS), frontend (Netlify), environment variables, database hosting | Deploy applications securely on cloud platforms |
Capstone Project | · Full stack app with frontend, backend, database, authentication, CRUD, and deployment | Apply all learned skills in a practical project |
Assessment Methods
Module | Assessment | What to Do |
Frontend | Quiz + Small Project | Test HTML/CSS/JS basics, build a simple webpage |
Python Basics | Coding Practice | Write small programs with loops and functions |
Backend | Mini Project | Create a basic app to show and add data |
Database | Quiz + Task | Write simple SQL queries and connect app to DB |
APIs | Build & Use API | Make a small API and fetch data using JS |
Git & Docker | Hands-on Tasks | Use Git for code and make a Docker container |
Deployment | Deployment Task | Put your app online (Heroku/Netlify) and keep secrets safe |
Final Project | Full App Project | Build and deploy a full app with login and data features |
Course Schedule
Module | Topics | Key Points | Duration |
1 | Frontend | HTML, CSS, JavaScript, React/Vue (optional) | 1 Week |
2 | Python Basics | Variables, Functions, OOP, File Handling | 1 Week |
3 | Backend | Flask or Django, Routing, Templates | 1 Week |
4 | Database | SQL, PostgreSQL/MySQL, ORM, MongoDB (optional) | 1 Week |
5 | APIs | REST APIs, Serialization, Fetch/Axios | 1 Week |
6 | Version Control & DevOps | Git, Docker, CI/CD basics | 1 Week |
7 | Deployment | Hosting on Heroku, Netlify, AWS, Security | 1 Week |
8 | Capstone Project | Full stack app development and deployment | 1 Week |
Software Requirements:
- VS Code
- PyCharm
- Sublime Text
- Chrome
- Firefox
- Bootstrap
- Tailwind CSS
- React
- js
- Python 3.x
- Flask
- Django
- PostgreSQL
- MySQL
- MongoDB
- pgAdmin
- MySQL Workbench
- MongoDB Compass
Instructor Name
Contact info
Certifications
- Enterprise Applications & Product Modernization Course
Learn to transform legacy enterprise systems into modern, scalable applications using cutting-edge architecture and cloud-native practices. This course covers software architecture, modernization strategies, API integration, DevOps, security, and real-world case studies.
Learning Outcomes
- Understand enterprise application domains and legacy challenges
- Design modern architectures: microservices, event-driven, API-first
- Assess legacy systems and create effective modernization roadmaps
- Implement Agile, DevOps, containerization, and Kubernetes orchestration
- Build and manage APIs and enterprise integrations
- Cloud-native transformation and migration strategies
- Modernize data platforms and pipelines
- Apply security best practices and ensure compliance
- Monitor and maintain application performance and reliability
Course structure Overview
Module | Topics Covered | Key Highlights |
Introduction to Enterprise Applications | · EA characteristics, Monolith vs Distributed, · ERP/CRM/SCM/HRMS, Legacy systems | Basics and challenges of enterprise apps |
Software Architecture Fundamentals | · Layered, SOA, Microservices, Event-driven, APIfirst design | Architecture styles and best practices |
Legacy System Assessment | · Modernization opportunities, Code analysis, Risk & impact Roadmaps | How to evaluate legacy |
Modern Development Practices | · Agile, DevOps, CI/CD, Docker, Kubernetes | Modern development & deployment methods |
API and Integration Strategies | · REST, GraphQL, Integration patterns, Kafka, · RabbitMQ, API gateways | Connecting and integrating enterprise systems |
Cloud-Native Transformation | · Cloud platforms, 12-factor apps, Serverless, · Migration strategies | Moving apps to the cloud |
Data Modernization | · Data lakes, ETL/ELT, Real-time pipelines, Modern DBs | Modern approaches to data management |
Security and Compliance in Modern Apps | · IAM, OAuth2, JWT, DevSecOps, GDPR, HIPAA, · ISO 27001 | Securing and complying with regulations |
Observability and Monitoring | · Logging, Monitoring, Tracing, Prometheus, · Grafana, ELK, APM | Keeping apps healthy and performant |
Product Modernization Case. Studies & Capstone | · Real-world cases, Modernization plans, · Architecture demos | Practical application and project presentation |
Assessment Methods
Module | Assessment Type | What to Do |
Introduction to EnterpriseApplications | Quiz | Test understanding of EA basics and legacy challenges |
Software Architecture Fundamentals | Quiz + Short Assignment | Identify architecture styles and design principles |
Legacy System Assessment | Case Study Analysis | Analyze a legacy system and suggest modernization steps |
Modern Development Practices | Practical Task | Set up CI/CD pipeline or containerize an app with Docker |
API and Integration Strategies | Mini Project | Build a simple API and integrate with a message broker |
Cloud-Native Transformation | Quiz + Assignment | Cloud concepts quiz and create a migration strategy |
Data Modernization | Task | Design a modern data pipeline or ETL process |
Security and Compliance | Quiz + Scenario Analysis | Test on security concepts and compliance case study |
Observability and Monitoring | Hands-on Task | Implement logging and monitoring using tools like Prometheus or ELK |
Capstone Project | Final Project + Presentation | Develop a modernization plan and present the solution |
Course Schedule
Module | Topics Covered | Duration |
Introduction to Enterprise Applications | Enterprise app basics, monoliths vs distributed systems, ERP, CRM, legacy challenges | 1 Week |
Software Architecture Fundamentals | Layered architecture, SOA, microservices, event-driven, API-first design | 1 Week |
Legacy System Assessment | Modernization opportunities, code analysis, risk & impact, roadmaps | 1 Week |
Modern Development Practices | Agile, DevOps, CI/CD, Docker, Kubernetes | 1 Week |
API and Integration Strategies | REST, GraphQL, integration patterns, Kafka, RabbitMQ, API gateways | 1 Week |
Cloud-Native Transformation | Cloud platforms, 12-factor apps, serverless, cloud migration | 1 Week |
Data Modernization | Data lakes, ETL/ELT, real-time pipelines, modern databases | 1 Week |
Security and Compliance in Modern Apps | IAM, OAuth2, JWT, DevSecOps, GDPR, HIPAA, ISO 27001 | 1 Week |
Observability and Monitoring | Logging, monitoring, tracing, Prometheus, Grafana, ELK, APM | 1 Week |
10. Product Modernization Case Studies & Capstone | Real-world cases, modernization plans, architecture demos, presentations | 1 Week |
Software Requirements
- Git
- GitHub
- Docker
- Kubernetes
- Jenkins or GitHub Actions (CI/CD tools)
- Apache Kafka
- RabbitMQ
- Kong or Apigee (API gateways)
- AWS / Azure / Google Cloud Platform (Cloud platforms)
- Prometheus
- Grafana
- ELK Stack (Elasticsearch, Logstash, Kibana)
- OAuth2 / OpenID Connect libraries
- Python / Java / Node.js (Programming languages)
- IDEs: VS Code, IntelliJ IDEA, PyCharm
Instructor Name
Contact info
Certifications
- Data Science & Artificial Intelligence Course
Master the foundations and advanced techniques in data science and AI to build intelligent, data-driven solutions.
This course covers Python programming, statistics, machine learning, deep learning, NLP, and AI ethics.
Learning Outcomes
- Data science lifecycle and AI fundamentals
- Python for data manipulation and visualization
- Core statistics and probability concepts
- Data preprocessing and feature engineering
- Supervised and unsupervised machine learning algorithms
- Deep learning with TensorFlow and Keras
- Natural Language Processing and transformer models
- Advanced AI topics: reinforcement learning, ethics, and generative AI
- Practical tools including Jupyter, Scikit-learn, and cloud AI platforms
Course structure Overview
Module | Topics Covered | Key Highlights |
Introduction to Data Science & AI
| · Data Science lifecycle, AI vs ML vs Deep Learning,Applications, Tools | Overview and basic concepts |
Python for Data Science | · Python basics, NumPy, Pandas, Data visualization, Working with datasets | Core Python & data manipulation |
Statistics and Probability | · Descriptive stats, Probability distributions,Hypothesis testing, Regression | Statistical foundations |
Data Wrangling and. Preprocessing | · Handling missing data, Normalization, Feature engineering, Data splitting | Preparing clean data for models |
Machine Learning | · Supervised & unsupervised learning, Algorithms, Model evaluation | Building and assessing ML models |
Deep Learning and Neural Networks | · Neural networks basics, TensorFlow/Keras, CNNs, · RNNs, Model tuning | Deep learning model building |
Natural Language Processing (NLP) | · Text preprocessing, Word embeddings, Sentiment analysis, Transformers | Processing and understanding text data |
8. AI and Advanced Topics | · Reinforcement learning, AI ethics, Explainable AI,Generative AI | Cutting-edge AI concepts and ethics |
9. Tools and Platforms | · Jupyter, Google Colab, Scikit-learn, TensorFlow, Git, Cloud platforms | Essential tools and environment setup |
10. Capstone Project | · Problem definition, EDA, Modeling, Deployment Presentation | End-to-end AI project experience |
Assessment Methods
Module | Assessment Type | What to Do |
1. Introduction to Data Science and AI | Quiz | Test understanding of basic concepts and tools |
2. Python for Data Science | Coding exercises | Write Python code for data manipulation and visualization |
3. Statistics and Probability | Quiz + Problem-solving | Solve statistics problems and apply probability concepts |
4. Data Wrangling and Preprocessing | Practical Task | Clean and prepare a real dataset |
5. Machine Learning | Mini Project | Build and evaluate ML models for classification/regression |
6. Deep Learning and Neural. Networks | Assignment | Create and train a neural network using TensorFlow/Keras |
7. Natural Language Processing (NLP) | Coding Task | Perform text preprocessing and sentiment analysis |
8. AI and Advanced Topics | Quiz + Case Study | Test advanced AI concepts and ethics scenarios |
9. Tools and Platforms | Hands-on Exercise | Use Jupyter, Git, and cloud tools for a small workflow |
10. Capstone Project | Final Project + Presentation | End-to-end AI solution development and demo |
Course Schedule
SI No. | Module | Topics | Activities | Duration |
1 | Introduction to Data Science and AI | Basics, lifecycle, AI vs ML vs DL | Lecture, Quiz | 1 Week |
2 | Python for Data Science | Python basics, NumPy, Pandas, Visualization | Coding exercises, Dataset practice | 1 Week |
3 | Statistics and Probability | Descriptive stats, probability, hypothesis testing | Quiz, Problem solving | 1 Week |
4 | Data Wrangling and Preprocessing | Handling missing data, normalization, feature engineering | Practical data cleaning task | 1 Week |
5 | Machine Learning | Algorithms,supervised/unsupervised learning, evaluation | Mini project on ML models | 1 Week |
6 | Deep Learning and Neural Networks | Neural nets, TensorFlow, CNNs, RNNs | Assignment:build/train neural network | 1 Week |
7 | Natural Language Processing (NLP) | Text preprocessing, embeddings, sentiment analysis | Coding task on NLP | 1 Week |
8 | AI and Advanced Topics | Reinforcement learning, AI ethics, XAI | Quiz, Case study | 1 Week |
9 | Tools and Platforms | Jupyter, Git, Cloud platforms | Hands-on workflow exercise | 1 Week |
10 | Capstone Project | Full AI project development and presentation | Project work and demo | 1 Week |
Software Requirement
- Python (Anaconda distribution recommended)
- Jupyter Notebook / JupyterLab
- Google Colab (optional, cloud-based)
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn
- TensorFlow
- Keras
- PyTorch (optional)
- Git
- GitHub
- VS Code or PyCharm (IDE)
- Cloud platforms: AWS SageMaker, Google AI Platform (overview)
Instructor Name
Contact info
Certifications
- DevOps Testing & CI/CD Pipeline Course
Learn how to streamline software delivery with DevOps practices, automated testing, and continuous integration and deployment.
Key Topics Covered:
- DevOps culture, principles, and lifecycle
- Software testing methods and automation (TDD, BDD)
- Building and managing CI/CD pipelines with Jenkins, GitLab, and others
- Deployment strategies and Infrastructure as Code (IaC)
- Containerization with Docker and orchestration using Kubernetes
- Automated testing: unit, integration, performance, and security
- Monitoring, logging, and incident management tools
- Security integration in DevOps pipelines (DevSecOps)
Course structure Overview
Module | Topics Covered |
1. Introduction to DevOps | · DevOps culture, principles, lifecycle, tools |
2. Software Testing Fundamentals | · Testing types, automated/manual testing, TDD, BDD, tools |
3. Continuous Integration (CI) | · CI concept, pipeline setup, code quality, automated tests |
4. Continuous Delivery and Deployment (CD) | · Delivery vs deployment, strategies, IaC, config management |
5. CI/CD Tools and Platforms | · Jenkins, GitLab CI/CD, CircleCI, Docker integration |
6. Containerization and Orchestration | · Docker basics, Docker Compose, Kubernetes, Helm charts |
7. Automated Testing in CI/CD | · Unit, integration, performance, security testing automation |
8. Monitoring and Logging | · Monitoring importance, Prometheus, Grafana, ELK, alerts |
9. Security in CI/CD Pipelines | · DevSecOps, security scans, secrets management, compliance |
10. Capstone Project | · Build full CI/CD pipeline, automate build/test/deploy, containerization, monitoring, rollback |
Assessment Methods
Module | Assessment Type | What to Do |
Introduction to DevOps | Quiz | Test understanding of DevOps principles and lifecycle |
Software Testing Fundamentals | Quiz + Practical task | Identify testing types and write test cases |
Continuous Integration (CI) | Practical exercise | Setup a basic CI pipeline with automated tests |
Continuous Delivery and Deployment (CD) | Assignment | Explain deployment strategies and configure IaC basics |
CI/CD Tools and Platforms | Hands-on task | Configure Jenkins or GitLab CI/CD pipeline |
Containerization and Orchestration | Practical project | Containerize app with Docker and deploy using Kubernetes |
Automated Testing in CI/CD | Coding + automation task | Automate unit and integration tests in CI pipeline |
Monitoring and Logging | Quiz + hands-on | Setup monitoring with Prometheus and create dashboards |
Security in CI/CD Pipelines | Quiz + scenario analysis | Implement security scans and manage secrets in pipelines |
Capstone Project | Final project + presentation | Build and demonstrate a full CI/CD pipeline with monitoring and rollback |
Course Schedule
Module No. | Module Title | Topics Covered | Duration |
1 | Introduction to DevOps | DevOps culture, principles, lifecycle, tools | 1 Week |
2 | Software Testing Fundamentals | Testing types, automated/manual testing, TDD, BDD, tools | 1 Week |
3 | Continuous Integration (CI) | CI concept, pipeline setup, code quality, automated tests | 1 Week |
4 | Continuous Delivery and Deployment (CD) | Delivery vs deployment, strategies, Infrastructure as Code (IaC), config management | 1 Week |
5 | CI/CD Tools and Platforms | Jenkins, GitLab CI/CD, CircleCI, Docker integration | 1 Week |
6 | Containerization and Orchestration | Docker basics, Docker Compose, Kubernetes, Helm charts | 1.5 Weeks |
7 | Automated Testing in CI/CD | Unit, integration, performance, and security testing automation | 1 Week |
8 | Monitoring and Logging | Monitoring importance, Prometheus, Grafana, ELK stack, alerting | 1 Week |
9 | Security in CI/CD Pipelines | DevSecOps, security scans, secrets management, compliance | 1 Week |
10 | Capstone Project | Build full CI/CD pipeline, automate build/test/deploy, containerization, monitoring, rollback | 1.5 Weeks |
Software Requirements:
- Git
- GitHub / GitLab
- Jenkins
- Docker
- Kubernetes
- Helm
- Ansible / Chef / Puppet (any one)
- Selenium
- JUnit / TestNG
- Postman
- SonarQube (code quality analysis)
- Prometheus
- Grafana
- ELK Stack (Elasticsearch, Logstash, Kibana)
- VS Code / IntelliJ / PyCharm (any IDE)
Instructor Name
Contact info
Certification
- Flutter Mobile Development Course
Master mobile app development using Flutter and Dart with hands-on experience building cross-platform apps.
Learning Outcomes:
- Introduction to Flutter framework and Dart language
- Core Flutter widgets, UI design, and theming
- State management with Provider and other patterns
- Navigation, routing, and data handling (REST APIs, Firebase)
- Creating animations and custom graphics
- Testing strategies for Flutter apps
- Preparing and deploying apps on Android and iOS
- CI/CD for Flutter apps
Course structure Overview
Module | Topic | Key Highlights |
1 | Introduction to Flutter | · What is Flutter & Dart, setup, comparison with native apps |
2 | Dart Programming Basics | · Dart syntax, OOP, async/await, streams |
3 | Flutter Widgets & UI Design | · Stateless/Stateful widgets, layout, Material/Cupertino design |
4 | State Management | · setState, Provider, overview of Bloc & Riverpod |
5 | Navigation and Routing | · Navigation APIs, data passing, named routes, deep linking |
6 | Working with Data | · REST APIs, JSON, local storage, Firebase basics |
7 | Animations and Graphics | · Basic and custom animations, drawing with CustomPainter |
8 | Testing Flutter Apps | · Unit, widget, integration tests, using Flutter DevTools |
9 | Deployment | · Build for Android/iOS, CI/CD, app publishing |
10 | Capstone Project | · Complete app with real features, storage, state, and animations |
Assessment Methods
Module | Assessment Type | What to Do |
1 | Quiz | Test understanding of Flutter basics & setup |
2 | Coding Task | Write Dart functions & use async/await |
3 | UI Design Task | Build a simple UI using layout widgets |
4 | Practical Exercise | Implement state management using setState & Provider |
5 | Hands-on Task | Create a multi-screen app with routing |
6 | API Integration Task | Fetch and display data from an API |
7 | Animation Task | Add animations to a screen using Flutter animations |
8 | Testing Assignment | Write basic unit and widget tests |
9 | Deployment Activity | Build and prepare app for Android/iOS deployment |
10 | Final Capstone Project | Complete and present a functional app with full features |
Course Schedule
SI No. | Module | Activities | Duration |
1 | Introduction to Flutter | Setup Flutter, run first app, quiz | 1 Week |
2 | Dart Programming Basics | Learn Dart syntax, write functions | 1 Week |
3 | Widgets and UI Design | Build UI, layout practice | 1 Week |
4 | State Management | Practice with Provider & setState | 1 Week |
5 | Navigation and Routing | Build multi-screen app | 1 Week |
6 | Working with Data | API & local storage integration | 1 Week |
7 | Animations and Graphics | Add basic/custom animations | 1 Week |
8 | Testing Flutter Apps | Unit & widget testing | 1 Week |
9 | Deployment | Package & test app, CI/CD intro | 1 Week |
10 | Capstone Project | Build full app, submit, present | 1.5 Weeks |
Software Requirements
- Flutter SDK
- Dart SDK
- Android Studio / VS Code
- Xcode (for iOS deployment on macOS)
- Android Emulator / iOS Simulator
- Flutter DevTools
- Postman (for API testing)
- Firebase Console (for backend services)
Instructor Name
Contact info
Certification
- Selenium Automation Testing Course: Learn to automate web applications using Selenium and industry-standard testing tools and frameworks.
Learning Outcomes:
- Introduction to test automation and Selenium suite
- Selenium WebDriver fundamentals (element handling, waits, alerts)
- Test frameworks: TestNG, JUnit, data-driven testing
- Project setup with Maven, version control with Git
- Page Object Model (POM) for maintainable test scripts
- Cross-browser and parallel test execution using Selenium Grid
- Integration with Jenkins for CI/CD automation
- Advanced topics: JavaScriptExecutor, file handling, BDD with Cucumber
Course structure Overview
Module | Topic | Description |
1 | Introduction to Test Automation | · Learn testing basics and the need for automation |
2 | Selenium WebDriver Basics | · Set up Selenium and write basic automation scripts |
3 | Advanced Selenium WebDriver | · Handle alerts, dropdowns, waits, and screenshots |
4 | Test Frameworks and Integration | · Use JUnit or TestNG to write and run test cases |
5 | Selenium with Maven and Git | · Manage projects using Maven and push code to GitHub |
6 | Page Object Model (POM) | · Build reusable, maintainable test code using POM |
7 | Cross-Browser and Parallel Testing | · Run tests on multiple browsers using Selenium Grid and TestNG |
8 | CI/CD Integration | · Automate test execution using Jenkins |
9 | Advanced Topics | · File upload/download, JavaScriptExecutor, and Cucumber for BDD |
10 | Capstone Project | · Complete automation of a real web app with framework and CI/CD integration |
Assessment Methods
Module | Assessment Type | What to Do |
1 | Quiz | Test basics of testing and Selenium |
2 | Practical Task | Write a script to automate a login page |
3 | Hands-on Task | Handle dropdowns, alerts, and use waits |
4 | Assignment | Create test cases using TestNG or JUnit |
5 | Coding Task | Setup Maven project and push to GitHub |
6 | Mini Project | Implement POM for a simple web app |
7 | Lab Activity | Run tests on Chrome and Firefox in parallel |
8 | CI/CD Task | Configure Jenkins to run tests automatically |
9 | Practice Task | Automate file upload/download and dynamic elements |
10 | Capstone Project | Full test automation project with framework and CI integration |
Course Schedule
SI No. | Module | Activities | Estimated Duration |
1 | Intro to Test Automation | Concepts of automation, install Selenium | 2 hours |
2 | Selenium WebDriver Basics | Write simple automation scripts | 3 hours |
3 | Advanced Selenium | Use waits, handle alerts and elements | 3 hours |
4 | Test Frameworks | Write test cases using TestNG/JUnit | 3 hours |
5 | Maven & Git | Manage project and code using Maven & Git | 2.5 hours |
6 | Page Object Model | Create reusable scripts with POM | 3 hours |
7 | Cross-Browser Testing | Run tests on multiple browsers and use Selenium Grid | 3 hours |
8 | CI/CD Integration | Automate tests with Jenkins | 3 hours |
9 | Advanced Topics | Automate downloads, uploads, use Cucumber | 3 hours |
10 | Capstone Project | Build full testing project with all concepts | 5 hours |
Software Requirements
- Java JDK
- Selenium WebDriver
- TestNG / JUnit
- Eclipse / IntelliJ IDEA
- Maven
- Git & GitHub
- ChromeDriver / GeckoDriver
- Jenkins
- Selenium Grid
Instructor Name
Contact info
certifications:
- Cloud Fundamentals & Security Course
Gain a solid foundation in cloud computing and learn how to secure workloads across AWS, Azure, and Google Cloud.
Learning Outcomes
- Core cloud concepts: IaaS, PaaS, SaaS, and deployment models
- Architecture essentials: virtualization, containers, serverless, storage, networking
- Identity & Access Management (IAM) and multi-factor authentication
- Cloud security: shared-responsibility model, encryption, network defenses, auditing
- Compliance and governance (GDPR, HIPAA, PCI-DSS) and cost management
- Securing cloud workloads, incident response, and disaster recovery
- Monitoring, logging, and automated security tooling (GuardDuty, Security Center, SCC)
- Infrastructure-as-Code security with Terraform and CloudFormation
Course structure Overview
Module | Topic | Key Highlights |
1 | Introduction to Cloud Computing | · Basics of cloud, service & deployment models, benefits, and risks |
2 | Major Cloud Providers Overview | · AWS, Azure, GCP basics and their core services |
3 | Cloud Architecture Basics | · VMs, containers, storage types, and networking in the cloud |
4 | Identity and Access Management (IAM) | · Roles, policies, MFA, and security best practices |
5 | Cloud Security Fundamentals | · Encryption, firewalls, VPNs, and monitoring security |
6 | Compliance and Governance | · Compliance standards, resource tagging, and cost controls |
7 | Securing Cloud Workloads | · Secure cloud resource setup, application & container security |
8 | Monitoring and Logging | · Use of cloud-native tools for monitoring, logging, and alerts |
9 | Security Tools and Automation | · Security services, IaC security, and automated compliance tools |
10 | Capstone Project | · Create and secure a cloud setup, with IAM, monitoring, and security assessment |
Assessment Methods
Module | Assessment Type | What to Do |
1–2 | Quiz | Answer MCQs on cloud models and providers |
3–4 | Practical Task | Set up a virtual machine and apply IAM roles |
5–6 | Case Study | Analyze a cloud security scenario and suggest improvements |
7–8 | Hands-on Lab | Configure monitoring, logging, and secure a cloud application |
9 | Mini Project | Use automation to enforce cloud security policies |
10 | Capstone Project | Design a secure cloud environment and present your solution |
Course Schedule
Week | Topic | Focus Activities | Duration |
1 | Introduction to Cloud Computing | Understand basic concepts, deployment models, benefits, risks | 1 Week |
2 | Major Cloud Providers Overview | Explore AWS, Azure, GCP services and security offerings | 1 Week |
3 | Cloud Architecture Basics | Study multi-tier architecture, shared responsibility model | 1 Week |
4 | Identity and Access Management (IAM) | Practice setting roles, permissions, policies in cloud environments | 1 Week |
5 | Cloud Security Fundamentals | Learn encryption, firewall configuration, network security | 1 Week |
6 | Compliance and Governance | Study GDPR, HIPAA, ISO standards, cloud audits | 1 Week |
7 | Securing Cloud Workloads | Hands-on securing apps, containers, VMs, serverless environments | 1 Week |
8 | Monitoring and Logging | Use tools like CloudWatch, Stackdriver, ELK stack for real-time insights | 1 Week |
9 | Cloud Security Tools and Automation | Automate scanning, patching, threat detection using open-source & cloud-native tools | 1 Week |
10 | Capstone Project | Plan and secure a cloud-based app, present IAM, security, and monitoring setup | 1.5 Weeks |
Software Requirement:
- AWS / Azure / GCP accounts (free tier)
- Terraform / AWS CloudFormation
- AWS IAM, GuardDuty / Azure Security Center
- CloudWatch / Azure Monitor / GCP Stackdriver
- Git & GitHub
- Visual Studio Code or any code editor
Instructor Name
Contact info
Certifications
- React Native Mobile Development Course
Build high-performance cross-platform mobile apps with React Native. This hands-on course takes you from JavaScript and React fundamentals to publishing polished Android and iOS applications.
Learning Outcomes
- Set up the React Native toolchain (Expo, Android Studio, Xcode)
- Create reusable components, style with Flexbox, and handle gestures
- Implement navigation (stack, tab, drawer) and deep linking
- Manage local and global state with Hooks, Context API, and Redux
- Fetch and store data securely (fetch/Axios, AsyncStorage, local DBs)
- Access device hardware (camera, GPS, sensors) via native modules
- Write unit, snapshot, and end-to-end tests with Jest & Detox/Appium
- Package, sign, and publish apps to Google Play and the App Store
Course structure Overview
Module | Title | Key Highlights |
Module 1 | Introduction to React Native | · Learn about React Native architecture, setup, and comparison with other tools. |
Module 2 | JavaScript and React Fundamentals | · Understand modern JavaScript, React components, props, state, and hooks. |
Module 3 | React Native Components and Styling | · Build user interfaces using core components and apply responsive styling. |
Module 4 | Navigation | · Implement screen-to-screen navigation using React Navigation library. |
Module 5 | State Management | · Manage app state using useState, Context API, and Redux. |
Module 6 | Networking and Data Handling | · Work with REST APIs, fetch and display data, handle JSON and errors. |
Module 7 | Device Features and Native Modules | · Access device features like GPS and camera using permissions and APIs. |
Module 8 | Testing React Native Apps | · Write unit and end-to-end tests using Jest and Detox/Appium. |
Module 9 | Deployment | · Prepare app for release and publish to Play Store or App Store. |
Module 10 | Capstone Project | · Build and present a complete app covering all learned concepts. |
Assessment Methods
Module | Assessment Type | What to Do |
1–2 | Quiz + Coding Exercise | Set up environment & build a basic component using props and state |
3–4 | Mini Project | Design a styled multi-screen app with navigation |
5–6 | Hands-on Task | Implement global state and fetch data from API |
7–8 | Lab Task | Access device features and write unit/snapshot tests |
9 | Deployment Task | Generate APK and publish to store or emulate using Expo |
10 | Capstone Project | Build a full app integrating API, navigation, state, and device features |
Course Schedule
Week | Topic | Focus Activities |
1 | Introduction to React Native | Setup environment, run first app, understand mobile app development lifecycle |
2 | JavaScript and React Fundamentals | Learn JS ES6+, JSX, props, state, and component lifecycle basics |
3 | Components and Styling | Create custom components, use StyleSheet, Flexbox layout |
4 | Navigation | Implement stack, tab, and drawer navigation using React Navigation |
5 | State Management | Use useState, Context API, and intro to Redux for managing app state |
6 | Networking and Data Handling | Fetch APIs, handle JSON data, error handling, and async operations |
7 | Device Features and Native Modules | Integrate camera, location, notifications using native modules and Expo |
8 | Testing React Native Apps | Unit and UI testing using Jest and React Native Testing Library |
9 | Deployment | Build and deploy apps to Android and iOS (Expo, EAS, or native build tools) |
10 | Capstone Project | Build and present a complete mobile app using learned concepts |
Software Requirements:
- js
- npm or yarn
- Expo CLI
- React Native CLI (optional)
- Android Studio / Xcode
- Visual Studio Code
- Axios
- Redux Toolkit
- Jest
- Detox or Appium (for E2E testing)
- Git & GitHub
Instructor Name
Contact info
Certifications
- Docker & Kubernetes Course
Learn to containerize applications with Docker and orchestrate them using Kubernetes. This hands-on course covers image creation, networking, storage, scaling, and deploying multi-service apps in production environments.
Learning outcomes
- Docker fundamentals, CLI, and Docker Compose
- Kubernetes architecture and resource management
- Deployments, scaling, and persistent storage
- Helm, Ingress, monitoring (Prometheus, Grafana), and RBAC security
Course structure Overview
Module | Title | Key Highlights |
1 | Introduction to Containerization | · Understand containers, benefits over VMs, and use cases. |
2 | Docker Fundamentals | · Learn Docker installation, CLI basics, and architecture. |
3 | Docker Images and Containers | · Create and manage Docker images and containers. |
4 | Docker Networking and Storage | · Explore container networking, volumes, and persistent storage. |
5 | Docker Compose | · Build multi-container apps using Docker Compose. |
6 | Introduction to Kubernetes | · Learn Kubernetes components and key concepts (pods, deployments, etc.). |
7 | Kubernetes Setup and CLI | · Install Kubernetes (Minikube/kind), use kubectl, and write YAML. |
8 | Deploying Applications on Kubernetes | · Deploy and manage apps using Deployments, Services, ConfigMaps, and Secrets. |
9 | Scaling and Updating Applications | · Implement autoscaling, rolling updates, and health checks. |
10 | Storage and Persistent Volumes | · Work with PVs, PVCs, and StatefulSets. |
11 | Kubernetes Advanced Topics | · Learn Helm, Ingress, monitoring, and Kubernetes security practices. |
12 | Capstone Project | · Containerize and deploy a full app, implement scaling, monitoring,storage |
Assessment Methods
Module | Assessment Type | What to Do |
Introduction to Containerization | Quiz | Answer basic questions on containers vs VMs, benefits |
Docker Fundamentals | Lab Task | Use Docker CLI commands to run and manage containers |
Docker Images and Containers | Assignment | Write and optimize Dockerfiles, build custom images |
Docker Networking and Storage | Lab Task | Set up container networking and use volumes |
Docker Compose | Mini Project | Define and deploy a multi-container app with Docker Compose |
Introduction to Kubernetes | Quiz | Test concepts like pods, deployments, services |
Kubernetes Setup and CLI | Lab Task | Practice kubectl commands, setup local cluster |
Deploying Applications on Kubernetes | Assignment | Create YAMLs for pods, services, configMaps |
Scaling and Updating Applications | Lab Task | Implement HPA, perform rolling updates and rollbacks |
Storage and Persistent Volumes | Assignment | Use PVCs, storage classes |
Kubernetes Advanced Topics | Quiz / Lab Task | Set up Ingress, use Helm, apply RBAC and monitoring tools |
Capstone Project | Project + Presentation | Deploy full app, include scaling, storage, logging; present setup |
Course Schedule
Week | Topic | Focus Activities | Duration |
1 | Introduction to React Native | Environment setup, run first app, overview of mobile dev lifecycle | 4–6 hours |
2 | JavaScript & React Fundamentals | ES6+, JSX, props, state, components | 6–8 hours |
3 | Components and Styling | Create reusable components, styling with Flexbox and StyleSheet | 6–7 hours |
4 | Navigation | React Navigation: Stack, Tab, Drawer navigation | 5–6 hours |
5 | State Management | useState, Context API, intro to Redux | 5–6 hours |
6 | Networking & Data Handling | Fetch API data, async/await, error handling | 6–7 hours |
7 | Device Features & Native Modules | Camera, location, notifications, native integration via Expo | 6–8 hours |
8 | Testing React Native Apps | Jest testing, component/UI tests, snapshot testing | 5–6 hours |
9 | Deployment | Build and publish to Play Store/App Store or deploy via Expo | 4–6 hours |
10 | Capstone Project | Design, build, and present a complete mobile app | 8–10 hours |
Software Requirements:
- Docker
- Docker Desktop
- Docker CLI
- Docker Compose
- Kubernetes Minikube or kind kubectl
- Helm
- Prometheus
- Grafana
- ELK Stack (Elasticsearch, Logstash, Kibana)
- Portainer
- K9s
Instructor Name
Contact info
Certifications
- Apache Spark, Kafka & Scala Training:Master Big Data processing with hands-on training in Scala, Apache Spark, and Apache Kafka. Learn to build scalable, real-time data pipelines from the ground up.
Learning Outcomes
- Scala fundamentals and functional programming
- Spark RDDs, DataFrames, SQL, and Structured Streaming
- Kafka architecture, producers/consumers, and message delivery
- Real-time data integration with Spark + Kafka
- Monitoring, fault tolerance, and optimization
Course structure Overview
Module No. | Module Title | Key Highlights |
1 | Introduction to Big Data Ecosystem | · Big Data overview, Spark, Kafka, Scala role |
2 | Scala Fundamentals | · Scala basics: syntax, data types, functions, OOP |
3 | Advanced Scala Concepts | · Case classes, pattern matching, traits, concurrency |
4 | Apache Spark Basics | · Spark architecture, RDDs, DataFrames, Spark SQL |
5 | Spark Advanced Topics | · Dataset API, streaming, window ops, tuning |
6 | Introduction to Apache Kafka | · Kafka architecture, brokers, topics, producers |
7 | Kafka Producer and Consumer APIs | · Writing Kafka producers/consumers in Scala |
8 | Integrating Spark with Kafka | · Spark Streaming with Kafka, data processing |
9 | Monitoring and Managing Kafka and Spark | · Monitoring tools, Spark UI, fault tolerance |
10 | Capstone Project | · Build and deploy streaming data pipeline |
Assessment Methods
Module | Assessment Type | What to Do |
1. Big Data Ecosystem | Quiz | Answer questions about Big Data, Spark, Kafka, Scala basics |
2. Scala Fundamentals | Coding Exercises | Write Scala programs using syntax, functions, collections |
3. Advanced Scala Concepts | Coding and Short Quiz | Solve problems using case classes, traits, Futures |
4. Apache Spark Basics | Practical Assignment | Use Spark to create RDDs, DataFrames, run queries |
5. Spark Advanced Topics | Project Task | Optimize Spark jobs, implement streaming tasks |
6. Apache Kafka Introduction | Quiz and Short Tasks | Explain Kafka architecture, create topics |
7. Kafka Producer/Consumer APIs | Coding Assignment | Write Kafka producers and consumers in Scala |
8. Spark-Kafka Integration | Hands-on Project | Build streaming app integrating Spark and Kafka |
9. Monitoring & Management | Case Study & Quiz | Analyze monitoring metrics and fault tolerance |
10. Capstone Project | Final Project | Build full streaming pipeline with Scala, Spark, Kafka |
Course Schedule
Module No. | Module Title | Key Activities | Estimated Duration |
1 | Introduction to Big Data Ecosystem | Overview lectures, ecosystem walkthrough, group discussions | 4–5 hours |
2 | Scala Fundamentals | Hands-on coding sessions, syntax practice, mini exercises | 6–8 hours |
3 | Advanced Scala Concepts | Pattern matching, collections, functional programming | 6–7 hours |
4 | Apache Spark Basics | Spark setup, RDDs, DataFrames labs | 7–8 hours |
5 | Spark Advanced Topics | Spark Streaming, tuning, structured APIs | 7–9 hours |
6 | Introduction to Apache Kafka | Kafka architecture, topics/partitions, cluster demo | 4–5 hours |
7 | Kafka Producer and Consumer APIs | Develop Kafka producers/consumers, test apps | 6–7 hours |
8 | Integrating Spark with Kafka | Stream pipeline coding, Spark Streaming with Kafka | 6–8 hours |
9 | Monitoring and Managing Kafka and Spark | Logging, metrics, fault tolerance labs | 5–6 hours |
10 | Capstone Project | Project planning, implementation, code walkthrough, demo | 10–12 hours |
Software Requirements:
- Scala
- Apache Spark
- Apache Kafka
- Zookeeper
- IntelliJ IDEA or any Scala IDE
- Maven or SBT (Scala build tools)
- Git (version control)
- Docker (optional, for containerization)
- Monitoring tools (Spark UI, Kafka Manager, etc.)
Instructor Name
Contact info
Certifications
- Snowflake, SQL & dbt Training: Learn to build scalable data pipelines with Snowflake, master SQL for analytics, and transform data using dbt.
Learning Outcomes:
- Snowflake architecture, data loading, and SQL querying
- Advanced SQL: joins, window functions, and CTEs
- Performance optimization and data governance
- dbt for modular transformations, testing, and documentation
- Workflow automation and integration with BI tools
Course Structure Overview
Module | Title | Key Topics Covered |
1 | Introduction to Snowflake | · Snowflake overview, architecture, environment setup |
2 | Snowflake Basics | · Databases, schemas, loading data, querying with SQL |
3 | Advanced SQL for Snowflake | · Joins, CTEs, window functions, transactions, time travel |
4 | Performance Optimization in Snowflake | · Clustering, caching, profiling, optimization |
5 | Introduction to dbt | · dbt setup, project structure, models, sources, macros |
6 | dbt Core Features | · Incremental models, snapshots, Jinja templating, testing |
7 | Advanced dbt Usage | · Project refactoring, hooks, dbt Cloud, CI/CD |
8 | Data Governance and Security | · RBAC, sensitive data, auditing, compliance in Snowflake |
9 | Integration and Ecosystem | · Connecting to BI tools, scheduling with Airflow or other tools |
10 | Capstone Project | · End-to-end data pipeline using Snowflake and dbt with optimization and documentation |
Assessment Methods
Module 1 | Quiz + Setup Task | Quiz on Snowflake architecture + set up Snowflake environment |
Module 2 | Lab Task | Load sample data and write basic SQL queries in Snowflake |
Module 3 | SQL Exercise | Write advanced SQL queries with joins, CTEs, and window functions |
Module 4 | Optimization Challenge | Analyze and improve query performance using profiling tools |
Module 5 | dbt Project Setup | Initialize a dbt project and connect to Snowflake |
Module 6 | SQL Model Implementation | Create modular models and document/test them in dbt |
Module 7 | Workflow Task | Automate dbt jobs using hooks and version control |
Module 8 | Case Study | Implement security and governance setup in Snowflake |
Module 9 | Integration Exercise | Connect Snowflake with BI tools and schedule dbt jobs |
Module 10 | Capstone Project | Build end-to-end data pipeline and present optimization/reporting |
Course Schedule
Module No. | Module Title | Key Activities | Estimated Duration |
1 | Introduction to Snowflake | Account setup, platform UI walkthrough | 3–4 hours |
2 | Snowflake Basics | Data loading (staging), basic querying labs | 4–5 hours |
3 | Advanced SQL for Snowflake | CTEs, window functions, joins, practice problems | 5–6 hours |
4 | Snowflake Performance Optimization | Query profiling, clustering, caching optimization | 4–5 hours |
5 | Introduction to dbt | Install dbt, init project, dbt Cloud vs Core | 3–4 hours |
6 | dbt Core Features | Model building, testing, documentation | 5–6 hours |
7 | Advanced dbt Usage | Refactoring models, exposures, CI/CD concepts | 4–5 hours |
8 | Data Governance and Security | Roles, policies, row-level security, auditing | 4–5 hours |
9 | Integration and Ecosystem | Connect with BI tools (Tableau, Looker), orchestration tools like Airflow | 4–5 hours |
10 | Capstone Project | End-to-end pipeline with dbt + Snowflake, test & optimize | 8–10 hours |
Software Requirements:
- Snowflake
- SQL (Snowflake SQL)
- dbt (Data Build Tool)
- Jinja (templating with dbt)
- Git (version control)
- Airflow (or similar orchestrator)
- BI tools (Tableau, Looker, optional for integration)
Instructor Name
Contact info
Certifications
- Tableau & Power BI Visualization: Master modern BI tools to build compelling dashboards and unlock insights through interactive data storytelling.
Learning Outcomes:
- Tableau & Power BI setup and interfaces
- Data connection, transformation, and modeling
- Charts, KPIs, maps, and advanced visualizations
- Dashboards, interactivity, and performance tips
- Sharing, collaboration, and security features
Course structure Overview
Module No. | Module Title | Key Highlights |
1 | Introduction to Data Visualization | · Importance of visualization, overview of Tableau and Power BI, dashboards, KPIs |
2 | Getting Started with Tableau | · Installing Tableau Desktop, connecting data sources, interface basics, simple chart creation |
3 | Data Preparation in Tableau | · Data blending, joining, filters, groups, calculated fields, working with dates and hierarchies |
4 | Advanced Visualization in Tableau | · Interactive dashboards, actions, tooltips, maps, advanced charts |
5 | Introduction to Power BI | · Installing Power BI Desktop, connecting data, interface overview, simple visualizations |
6 | Data Modeling in Power BI | · Power Query Editor, data transformation, table relationships, introduction to DAX |
7 | Advanced Power BI Visualizations | · Reports and dashboards, slicers, custom visuals, drill-through, bookmarks |
8 | Sharing and Collaboration | · Publishing dashboards and reports, row-level security, collaboration features |
9 | Performance Optimization | · Dashboard design best practices, query and data refresh optimization, handling large datasets |
10 | Capstone Project | · End-to-end dashboard using Tableau and Power BI, multi-source integration, storytelling |
Assessment Methods
Module No. | Assessment Type | What to Do / Focus Area |
1-2 | Quiz | Basic concepts of data visualization, Tableau and Power BI fundamentals |
3-4 | Practical Assignment | Create visualizations and dashboards in Tableau |
5-6 | Quiz + Hands-on Task | Power BI basics, data modeling, and simple reports |
7 | Practical Assignment | Advanced Power BI reports with slicers, bookmarks, and custom visuals |
8 | Case Study / Report | Sharing, security, and collaboration features |
9 | Short Quiz | Performance optimization best practices |
10 | Capstone Project | Build and present a full dashboard project integrating multiple data sources and interactivity |
Course Schedule
Module No. | Module Title | Activities | Estimated Duration |
1 | Introduction to Data Visualization | Overview, concepts, tools introduction | 2–3 hours |
2 | Getting Started with Tableau | Install Tableau, connect data, create basic charts | 3–4 hours |
3 | Data Preparation in Tableau | Data blending, calculated fields, filters | 3–4 hours |
4 | Advanced Visualization in Tableau | Dashboards, maps, interactive charts | 4–5 hours |
5 | Introduction to Power BI | Install Power BI, connect data, basics | 3–4 hours |
6 | Data Modeling in Power BI | Power Query, relationships, introduction to DAX | 4–5 hours |
7 | Advanced Power BI Visualizations | Dashboards, custom visuals, drill-through | 4–5 hours |
8 | Sharing and Collaboration | Publishing, sharing, security, collaboration | 3 hours |
9 | Performance Optimization | Dashboard best practices, data refresh | 3 hours |
10 | Capstone Project | Build integrated dashboards, present insights | 6–8 hours |
Software Requirements:
- Tableau Desktop
- Tableau Server / Tableau Online (for sharing)
- Power BI Desktop
- Power BI Service (for sharing)
- Excel (or other data sources)
- SQL (optional for data connection)
Instructor Name
Contact info
Certifications
- Azure Databricks :Learn to build scalable data pipelines and analytics solutions using Azure Databricks and Apache Spark.
Learning Outcomes:
- Databricks setup and Spark fundamentals
- Data ingestion and ETL with DataFrames and SQL
- Delta Lake for reliable, ACID-compliant data storage
- Streaming, ML basics, and Power BI integration
- Azure service integration, security, and cost optimization
Course structure Overview
Module No. | Module Title | Key Highlights |
1 | Introduction to Azure Databricks | · Databricks overview, Spark basics, architecture |
2 | Setting Up Environment | · Workspace creation, cluster types, user roles |
3 | Apache Spark Basics | · RDDs, DataFrames, Datasets, Spark SQL, notebooks |
4 | Data Ingestion and ETL | · Loading data, transformations, writing data sinks |
5 | Delta Lake | · ACID transactions, time travel, schema evolution |
6 | Advanced Data Processing | · Streaming, window functions, MLlib basics |
7 | Integration with Azure Services | · Azure Data Factory, Synapse, Power BI, AAD security |
8 | Monitoring and Management | · Cluster monitoring, auto-scaling, job scheduling |
9 | Collaborative Development | · Notebooks collaboration, Git integration, workflows |
10 | Capstone Project | · Build ETL pipeline, Delta Lake use, data analysis & viz |
Assessment Methods:
Module No. | Assessment Type | Description/What to Do |
1 | Quiz | Basics of Databricks, Spark, architecture concepts |
2 | Practical Assignment | Setup Databricks workspace, create clusters, assign roles |
3 | Hands-on Lab | Use Spark Core concepts with RDDs, DataFrames, and SQL |
4 | Project Task | Build ETL pipeline: data ingestion and transformation |
5 | Practical Exercise | Implement Delta Lake features: transactions, time travel |
6 | Lab/Assignment | Use Structured Streaming, window functions, basic ML models |
7 | Integration Project | Connect Databricks with Azure services (Data Factory, Power BI) |
8 | Monitoring Exercise | Configure cluster monitoring, auto-scaling, job monitoring |
9 | Group Activity | Collaborative notebook use, Git version control |
10 | Capstone Project | Complete ETL pipeline with Delta Lake and data visualization |
Course Schedule
SI No. | Topics Covered | Activities / Deliverables | Duration |
1 | Introduction to Azure Databricks | Quiz on basics | 1.5 hours |
2 | Setting Up Azure Databricks Environment | Practical setup of workspace and clusters | 2 hours |
3 | Basics of Apache Spark on Databricks | Hands-on lab with RDDs, DataFrames, Spark SQL | 3 hours |
4 | Data Ingestion and ETL Pipelines | Build simple ETL pipeline | 3 hours |
5 | Delta Lake on Azure Databricks | Implement Delta Lake features | 2.5 hours |
6 | Advanced Data Processing | Lab on streaming and MLlib basics | 3 hours |
7 | Integration with Azure Services | Project integrating Databricks with Azure services | 3 hours |
8 | Monitoring and Management | Configure monitoring and auto-scaling | 2 hours |
9 | Collaborative Development | Group work on notebooks and Git | 2 hours |
10 | Capstone Project | Build and present end-to-end ETL pipeline | 6 hours |
Software Requirements
- Azure Databricks
- Apache Spark
- Azure Blob Storage
- Azure Data Lake Storage
- Delta Lake
- Azure Data Factory
- Azure Synapse Analytics
- Power BI
- Git/GitHub
- Databricks CLI/Notebook environment
Instructor Name
Contact info
Certifications
- Data Modeling & AI Databases :Master modern data-modeling techniques and explore the next generation of AI-enabled databases in this hands-on, project-driven course.
Learning Outcomes:
- ER, conceptual, logical & physical modeling; normalization vs. denormalization
- Star/snowflake schemas, slowly-changing dimensions & data-vault design
- NoSQL & NewSQL fundamentals (document, key-value, columnar, graph)
- AI-powered data management: autonomous indexing, intelligent query tuning, self-driving DBs
- Knowledge graphs, RDF/SPARQL, ontologies & semantic modeling
- Data preparation for machine learning: feature engineering, versioning, dimensionality reduction
- Practical platforms: BigQuery ML, Azure Synapse, Snowflake AI features & pipeline integration
Course structure Overview
Module | Title | Key Highlights |
1 | Introduction to Data Modeling | · Data-modelling basics, conceptual / logical / physical models , ER diagrams,normalisation |
2 | Advanced Data Modeling Techniques | · Star & snowflake schemas, facts & dimensions, SCD, data-vault modeling |
3 | Introduction to AI Databases | · AI/ML-focused databases, graph DBs, knowledge graphs, key use cases |
4 | NoSQL and NewSQL Databases | · Doc, key-value, columnar, graph, NewSQL overview, · MongoDB & Cassandra basics |
5 | AI-Powered Data Management | · AI-driven indexing & optimization, automated cleaning, intelligent query processing |
6 | Knowledge Graphs & Semantic Data Modeling | · Knowledge-graph concepts, RDF, SPARQL, ontologies, practical tools |
7 | Data Modeling for Machine Learning | · Feature engineering, handling missing data, dimensionality reduction, versioning |
8 | Practical AI Database Platforms | · BigQuery ML, Azure Synapse, Snowflake AI features, ML pipeline integration |
9 | Case Studies & Industry Applications | · Real-world AI database projects, design patterns, common challenges |
10 | Capstone Project | · Design & implement an AI-driven data model, optimization, presentation |
Assessment Methods
Module | Assessment Type | What to Do |
Module 1 | Quiz + Diagram Exercise | Short quiz on data modeling terms + draw ER diagram from a use case |
Module 2 | Hands-on Assignment | Design a star/snowflake schema for a sample data warehouse |
Module 3 | MCQ + Use Case Analysis | Quiz on AI DB concepts + analyze which DB fits which AI scenario |
Module 4 | Lab Task | Create and query a basic NoSQL database using MongoDB or Cassandra |
Module 5 | Use Case Discussion | Explain how AI automates indexing or optimization in databases |
Module 6 | SPARQL Query Practice | Write RDF triples and run basic SPARQL queries |
Module 7 | Mini Project | Prepare ML-ready dataset: apply preprocessing and feature selection |
Module 8 | Tool Demo/Walkthrough | Explore BigQuery ML / Synapse and explain key AI features |
Module 9 | Case Study Presentation | Present a real-world use case involving AI-driven data modeling |
Module 10 | Capstone Project | Full data modeling solution + integration with AI/ML pipeline + presentation |
Course Schedule
Module | Topics Covered | Estimated Duration |
1 | Introduction to Data Modeling ER models, schema types, normalization, keys | 3 hours |
2 | Relational Databases & SQL Tables, relationships, joins, SQL queries | 4 hours |
3 | Dimensional Modeling Star and snowflake schemas, facts and dimensions | 3 hours |
4 | NoSQL & Big Data ModelsMongoDB, Cassandra, key-value and graph databases | 3.5 hours |
5 | AI & Machine Learning Data Needs Structured/unstructured data, labeling, training data | 3 hours |
6 | AI-Optimized Databases Vector databases (FAISS, Pinecone), graph & time-series databases | 3 hours |
7 | Data Pipelines & Storage ETL, data lakes, cloud storage (AWS, GCP, Azure) | 3.5 hours |
8 | Capstone Project Create a data model, choose database, present solution | 5 hours |
Software Requirements:
- io / Lucidchart (for ER diagrams)
- MongoDB / MongoDB Atlas
- Apache Cassandra
- Neo4j (for graph database modeling)
- Apache Jena / RDFLib (for RDF & SPARQL)
- Google BigQuery ML
- Azure Synapse Analytics
- Snowflake
- Python (Pandas, scikit-learn) (for ML feature prep) Jupyter Notebooks
Instructor Name
Contact info
Certifications
Curriculum
- 0m Duration
