Full Stack Data Science with Generative AI & Agentic AI

Key Features of Full Stack Data Science with Gen AI & Agentic AI

• End-to-End Data Science Stack: Covers data engineering, analysis, machine learning, deep learning, and deployment in a single course.
• Hands-On with Generative AI: Learn how to use LLMs like GPT-4, Claude, and Gemini to generate text, code, synthetic data, and insights.
• Build Agentic AI Systems: Master LangChain, AutoGPT, and CrewAI to create autonomous agents for data workflows and smart automation.
• Industry-Relevant Projects: Work on real-world use cases including AI chatbots, automated dashboards, and ML-powered web apps.
• ML & Deep Learning Foundation: Gain strong skills in scikit-learn, TensorFlow, PyTorch, and XGBoost for core predictive modeling.
• Integrated MLOps and Deployment: Learn to deploy models using FastAPI, Streamlit, Docker, and CI/CD tools on AWS or Hugging Face.
• Custom GPTs and Tool Integration: Build your own GPT-based tools and connect them with databases, APIs, and cloud storage.

Before Learning Full Stack Data Science with Gen AI & Agentic AI:

To succeed in this course, it’s helpful to have the following foundational skills and knowledge:

• Basic Programming Knowledge: Understanding of Python syntax, variables, functions, and loops is essential.
• Mathematics & Statistics: Familiarity with basic algebra, probability, and statistical concepts like mean, median, standard deviation, and correlation.
• Logic & Problem Solving: Ability to break down problems and write simple algorithms will help in data processing and model design.
• Understanding of Web & APIs (Optional but useful): Knowing how the internet, HTTP requests, and APIs work helps with full stack and agent-based automation modules.
• Basic Data Handling: Awareness of Excel, CSVs, or databases like MySQL or PostgreSQL is a plus.
• English Reading Skills: Since many tools, documentation, and AI models are in English, the ability to read and understand instructions is helpful.

1: Python for Data Science

• Python Basics, Data Types, Functions
• Working with Lists, Dictionaries, Loops, and Conditionals
• NumPy and Pandas for Data Manipulation
• Matplotlib and Seaborn for Visualization

2: SQL & Data Engineering

• SQL Basics, Joins, Aggregations
• Working with Databases (MySQL, PostgreSQL)
• Data Pipelines with Pandas and Airflow
• Connecting Python to SQL (SQLAlchemy)

3: Exploratory Data Analysis (EDA)

• Understanding Data Types and Structures
• Handling Missing Values and Outliers
• Correlation, Grouping, and Pivoting
• Automated EDA Tools (Pandas Profiling, Sweetviz)

4: Machine Learning Fundamentals

• Supervised vs Unsupervised Learning
• Regression, Classification, Clustering
• Model Evaluation Metrics
• Scikit-learn and XGBoost

5: Deep Learning Essentials

• Neural Networks with TensorFlow and Keras
• Image Classification with CNNs
• Time Series and RNNs
• Model Optimization and Regularization

6: Generative AI (Gen AI)

• Understanding LLMs (ChatGPT, Claude, Gemini)
• OpenAI API and Hugging Face Transformers
• Prompt Engineering & Few-Shot Learning
• Text Generation, Summarization, Embedding Models
• Synthetic Data Generation for ML Models

7: Agentic AI & Tools

• What is Agentic AI? Introduction to Agents
• LangChain and Function Calling
• Memory, Planning, and Multi-Step Task Execution
• LangGraph, AutoGPT, CrewAI, OpenAgents
• Building Autonomous Data Science Workflows

8: NLP & Text Analytics

• Tokenization, Lemmatization, Vectorization
• TF-IDF, Word2Vec, Sentence Embeddings
• Text Classification and Topic Modeling
• Question Answering with LLMs

9: MLOps and Deployment

• Model Deployment with Flask & FastAPI
• Dockerization and REST APIs
• MLflow for Model Tracking
• CI/CD Pipelines (GitHub Actions or Jenkins)