AIR-RES 2025

The fuel that supercharges machine learning is data. Data Science serves as the foundation for Artificial Intelligence (AI), providing the methodologies necessary to extract meaningful insights from vast amounts of data. Data Science involves collecting, cleaning, analyzing, and modeling data, which are essential steps in training AI systems to learn and make decisions. Through data, AI tools convert data to information, and then information is mapped to knowledge. In the context of AI, Data Science drives the creation of machine learning models, enabling systems to recognize patterns, understand language, and perform non-trivial tasks. Techniques such as exploratory data analysis, feature engineering, and statistical modeling are integral to developing robust AI applications. Furthermore, advancements in big data, cloud computing, and data visualization tools have expanded the potential of Data Science, allowing AI systems to process enormous datasets with speed and accuracy. The symbiotic relationship between Data Science and AI is revolutionizing industries, fostering innovations in healthcare, finance, transportation, and beyond. The list of topics below is by no means meant to be exhaustive:

- Generative AI and Data Augmentation
- Statistical Methods and Data Analytics
- Time Series Forecasting
- Explainable AI (XAI) and Model Interpretability
- Data Preprocessing and Cleaning Techniques
- Data Visualization
- Machine Learning and AI in Data Science
- Supervised and Unsupervised Learning Methods
- Deep Learning for Data Analysis
- Reinforcement Learning in Data Science
- Exploratory Data Analysis (EDA)
- Distributed Data Processing (Hadoop, Spark)
- Stream Processing
- Cloud Computing & Edge Computing for Big Data Applications
- Data Warehousing and ETL Processes
- Database Management Systems (SQL and NoSQL)
- Bias and Fairness in Data Analytics
- Ethical AI and Data-Driven Decision-Making
- Synthetic Data Generation
- Natural Language Processing (NLP) and Text Mining
- Internet of Things (IoT)
- Optimization Techniques in Data Science
- Open-Source Data Science Tools (Python, R, etc.)
- AI Frameworks for Data Science (TensorFlow, PyTorch)
- Business Intelligence Tools (Tableau, Power BI)
- Data Science in Kubernetes and Containerized Environments
- Applications of Data Science (Healthcare and Genomics, Financial Analytics, Fraud Detection, Social Media and Sentiment Analysis, Recommender Systems and Personalization, ...)