The Data Science course is an extensive program that introduces students to data analysis, artificial intelligence, and machine learning using Python. The training begins with the fundamentals of data science, including the data science lifecycle and important Python libraries such as NumPy, Pandas, and Matplotlib. Students learn how to clean, transform, and analyze datasets while applying statistical concepts like probability distributions, hypothesis testing, and exploratory data analysis. After building a strong analytical foundation, the course moves into artificial intelligence topics such as intelligent agents, search algorithms, game theory, and logic-based systems. The machine learning section introduces supervised and unsupervised learning techniques including regression models, classification algorithms, clustering, decision trees, and ensemble methods like random forests. The course then progresses to deep learning concepts such as neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and LSTM for sequence data. Students also explore transformer models used in natural language processing. Finally, the course covers model evaluation, deployment, and MLOps practices where students learn to create APIs and deploy machine learning models using frameworks like Flask or FastAPI.