FOUNDATION:

  • IT Fundamentals & Computational Thinking.
  • Professional Development Environment Setup.


DATA SCIENCE CORE:

  • Advanced Python Programming.
  • SQL Databases & Data Management.
  • Exploratory Data Analysis (EDA).


MACHINE LEARNING:

  • Supervised & Unsupervised Learning Models.
  • Feature Engineering & Model Evaluation.
  • Predictive Analytics Applications.


DEEP LEARNING:

  • Neural Networks Architectures.
  • Computer Vision Fundamentals.
  • Natural Language Processing (NLP).


GENERATIVE AI (GenAI):

  • Large Language Models (LLMs) Fundamentals.
  • Prompt Engineering & Prompt Design Techniques.
  • Fine-tuning models and working with OpenAI, Anthropic, or Open Source (Llama) APIs.


AI AGENTS & AUTOMATION:

  • Building Autonomous AI Agents (e.g., using LangChain or CrewAI).
  • Task Planning and Multi-Agent Systems.
  • Integration of AI Agents into business workflows.


DATA ENGINEERING:

  • Web Scraping & Data Mining for AI Training.
  • Data Pipeline Orchestration.