Focus and Scope

1. Data Science Fundamentals:

     a. Data acquisition, processing, management, and integration
     b. Exploratory data analysis and large-scale data visualization
     c. Computational statistics and mathematical modeling for data
     d. Data ethics, privacy, security, and governance

2. Artificial Intelligence Methodologies:

    a. Machine Learning: supervised, unsupervised, semi-supervised, and reinforcement learning
    b. Deep Learning: convolutional, recurrent, transformer, and generative models
    c. Natural Language Processing (NLP) and language understanding
    d. Computer Vision and pattern recognition
    e. Expert systems, reasoning, and knowledge representation

3. Advanced Analytics and Predictive Modeling:
    a. Predictive, prescriptive, and diagnostic analytics
    b. Algorithm development for big data analytics
    c. Data Mining and knowledge discovery
    d. Time series analysis and recommender systems

4. AI in Practice and Applications:
    a. AI applications across sectors such as healthcare, finance, education, manufacturing, e-commerce, transportation, and more
    b. Intelligent systems, human-AI interaction, and explainable AI (XAI)
    c. Edge AI and federated learning
    d. Reinforcement learning for autonomous systems and robotics
    e. Innovations in AI hardware and software