This module introduces Python programming fundamentals for data science, covering core concepts such as data types, loops, error handling, and functions. Learners gain hands-on experience with Python to build confidence for analytics workflows.

This module covers SQL from basics to advanced level, teaching learners to manage databases, store, retrieve, and manipulate data. It equips aspiring developers and analysts with essential backend data handling skills.

This module establishes a strong statistical foundation for data science, covering measures of central tendency, dispersion, skewness, correlation, regression, and distribution. Learners develop the ability to make data-driven predictions through statistical inference.

This module focuses on practical Python tools and techniques for data science roles, offering a blended learning approach with hands-on exploration of data science concepts and real-world applications.

This module delivers comprehensive machine learning training through 40+ hours of applied learning and interactive labs, covering essential ML concepts including supervised and unsupervised learning, ensemble methods, and model building with hands-on projects.

This module covers deep learning fundamentals and advanced architectures using leading AI/ML frameworks. Topics include neural networks, backpropagation, CNNs, RNNs, autoencoders, transfer learning, performance tuning, and interpretability using TensorFlow 2, Keras, and PyTorch.

This capstone module challenges learners to apply their full range of acquired skills to comprehensive, industry-oriented projects using real-world datasets and ETL processes, resulting in a professional portfolio that demonstrates practical data science expertise.

This module explores generative AI principles and applications including GANs, VAEs, large language models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), and model fine-tuning, preparing learners for cutting-edge AI implementation.

This module focuses on data visualization and business intelligence using Microsoft Power BI enhanced with AI capabilities, enabling learners to analyze data, generate visual insights, and create impactful reporting dashboards.

This module covers the complete MLOps lifecycle including scalable ML workflow automation, CI/CD pipeline design, Infrastructure as Code (IaC), experiment tracking, and production model deployment and monitoring using AWS services like SageMaker and CloudWatch.

This module provides hands-on experience with Microsoft Azure cloud services for building robust, scalable MLOps pipelines. Learners automate workflows using Azure Machine Learning jobs and deploy models through GitHub Actions for continuous integration and delivery.

This module teaches learners to build and manage machine learning solutions on the Microsoft Fabric platform, covering data preprocessing with Data Wrangler, model training and tracking with MLflow, batch prediction generation, and integrated data analytics and AI workflows.