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Course Brief – Full Stack Development with Python

This course equips learners with end-to-end full stack development skills using Python (Flask or Django), SQLite or PostgreSQL, and front-end technologies (HTML, CSS, JS). Emphasis is placed on version control with Git/GitHub, Agile workflows, real-life hands-on projects, and deploying production-ready applications. Suitable for career changers, fresh graduates, and aspiring developers.

Key Competencies & Learning Outcomes (All Tracks)

By the end of the course, learners will be able to:

• 🔧 Technical Proficiency
  Build web apps using Flask or Django with templating, routing, and DB integration.
  Implement CRUD operations, user authentication, and REST APIs.
  Use HTML/CSS/JavaScript for the frontend with Bootstrap for styling.
  Deploy apps using Render, Heroku, or Netlify.
• 💻 Collaboration & Deployment
  Work with GitHub repositories, branching, and team collaboration.
  Follow Agile methodology: sprints, user stories, MVP-first planning.
  Complete and demo a live deployed project.
• 🧰 Toolchain Mastery
  Git, GitHub, VS Code, SQLite, Postman, Bootstrap, Render/Vercel.
  For 9-month track: Docker, PostgreSQL, CI/CD pipelines.

Course Brief

This online course is designed for individuals seeking to enter or pivot into AI and software roles. It covers the fundamentals and implementation of Large Language Models (LLMs), Agentic AI architectures, LangChain, and Python-based AI engineering. Through hands-on project development, learners will build and deploy agentic systems powered by models like OpenAI’s GPT, Anthropic Claude, or similar.

Three Tracks:

• 8 Weeks (Training + Project) – Core concepts, Agile, GitHub, build & deploy 1 project
• 24 Weeks (Bootcamp) – Foundation + tools + LLMs + agents + APIs + deployment + capstone
• 36 Weeks (Internship track) – Bootcamp + advanced use cases, team projects, DevOps, optimization

Key Competencies & Learning Outcomes by End of Course

All learners will gain:

• 🔧 Technical Skills
  Build LLM-powered apps using LangChain
  Understand agentic behavior: tools, memory, chains, APIs
  Work with retrieval-augmented generation (RAG) and vector databases
  Deploy AI apps using Streamlit, FastAPI, and cloud platforms
• 💻 Engineering Practices
  Use GitHub for version control & collaboration
  Apply Agile methodology for project planning
  Design & deliver a live project with user-facing functionality
• 🎯 Project Execution
  Deliver at least one functional agentic app (2 months)
  Complete one end-to-end capstone project (6 months)
  Extend systems with DevOps, optimization, multi-agent support (9 months)

Course Brief:

This course is tailored for aspiring data analysts, career switchers, and students looking to break into the IT or analytics industry. It combines theoretical knowledge and hands-on experience in Python-based data analytics, including data preprocessing, EDA, predictive modeling, visualization, text analytics, and time series analysis.

Three progressive tracks are offered:

• 🟢 2-Month Track: Short-term training + 1 analytics project (Agile + GitHub + deployment)
• 🟡 6-Month Bootcamp: Complete Data Analytics curriculum with capstone
• 🔵 9-Month Internship Track: All of bootcamp + advanced analytics topics + DevOps + business use cases

Key Competencies & Learning Outcomes (All Tracks)

• 🧠 Technical Skills
  Master Python for data analysis (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Statsmodels)
  Perform data cleaning, feature engineering, EDA, hypothesis testing
  Build predictive models and evaluate them using real-world datasets
  Conduct text analytics and time series forecasting
  Create compelling dashboards and data stories using visual libraries or tools like Power BI/Plotly
• 💼 Professional Practices
  Follow Agile workflows with sprint planning and retrospectives
  Use Git and GitHub for version control, collaboration, and documentation
  Participate in live projects, presenting a final capstone to showcase skills
• 🚀 Outcomes
  Build a portfolio of at least 2–4 projects (depending on track)
  Deploy one live data project with source code and documentation
  Be job-ready for roles like Data Analyst, Junior Data Scientist, or BI Analyst

Course Brief
Title: GenAI & Automation with Python – Training + Internship

Overview:
A hands-on, career-switcher-friendly course designed to equip learners with skills in Python, automation tools, and Generative AI-powered workflows. Ideal for those entering IT or reskilling for modern tech roles.

Key Competencies & Outcomes
By the end of this course, learners will be able to:

• Build real-world Python automation scripts
• Integrate GenAI tools using APIs (OpenAI, Hugging Face, LangChain)
• Implement RPA using libraries like Selenium or PyAutoGUI
• Use GitHub and Agile methodologies in real-time project workflows
• Complete a capstone automation project with GenAI
• Deploy a solution and present in a live demo
• Gain internship-level experience through project simulation

Course Brief
Title: AI & IoT – Smart Systems Development

Overview:
A hands-on program designed to bridge hardware and intelligent software. Ideal for learners aiming to develop AI-enabled IoT systems, this course covers microcontrollers, sensor integration, real-time data processing, and deploying machine learning models at the edge or in the cloud.

Key Competencies & Outcomes
By the end of this course, learners will be able to:

• Work with IoT hardware (Arduino, Raspberry Pi) and connect common sensors
• Process and analyze sensor data using Python and edge computing techniques
• Deploy AI models to classify, predict, or control IoT data in real-time
• Build smart systems (e.g., automation, monitoring, alerts) using AI + IoT workflows
• Use cloud platforms (like AWS IoT or Azure IoT Hub) for remote data and control
• Follow Agile and GitHub practices to manage hardware-software projects
• Complete a final project demonstrating an AI-powered IoT prototype

Course Goal:
To prepare aspirants to clear PCEP (Certified Entry-Level Python Programmer) and PCAP (Certified Associate in Python Programming) certifications confidently, with hands-on coding, quizzes, and mock tests.

📚 Learning Objectives
By the end of this course, learners will be able to:

• Understand basic and intermediate Python programming constructs
• Apply control structures, functions, OOP, modules, and file operations
• Use Python Standard Library effectively
• Solve real-world problems using Python
• Confidently attempt PCEP and PCAP exams through mock assessments