This course is designed to equip learners with the practical skills and theoretical knowledge required to deliver end-to-end data science solutions on Microsoft Azure. Participants will gain valuable experience in the following areas:

• Gain insights into the core principles of data science, including data exploration, data cleaning, statistical analysis, and the role of machine learning.
• Learn how to navigate the Azure platform and utilize various Azure services, such as Azure Machine Learning, Azure Data Factory, and Azure Databricks, for data science applications.
• Master data preprocessing techniques including data collection, cleaning, transformation, and normalization, ensuring that high-quality data is used for model training.
• Understand how to build, train, evaluate, and deploy machine learning models using Azure Machine Learning, including supervised and unsupervised learning approaches.
• Learn to integrate Azure Cognitive Services (e.g., Text Analytics, Computer Vision) into data science solutions to add advanced capabilities such as natural language processing and image recognition.
• Develop skills to create meaningful data visualizations using tools like Power BI and Matplotlib, enabling effective communication of data insights to stakeholders.
• Explore best practices for designing data science projects, including defining project scopes, understanding stakeholder requirements, and ensuring alignment with business objectives.

• Familiarity with Data Science Concepts: A basic understanding of data science principles, and programming using Python is recommended.
• Introduction to Microsoft Azure: While prior experience with Azure is not mandatory, having a foundational knowledge of Azure's services will enhance the learning experience. Completion of a course like "Azure Fundamentals" may be helpful.

This training will equip you for the following job roles and career paths:

• Data Scientist
• Machine Learning Engineer
• AI Solutions Architect
• Cloud Data Engineer
• Business Intelligence Analyst
• Data Engineer

Module 1. Introduction to Data Science and Azure

• Overview of Data Science concepts and methodologies
• Introduction to Microsoft Azure and its significance in data science
• Setting up an Azure account and navigating the Azure portal

Module 2. Data Exploration and Preparation

• Understanding data sources and types
• Data cleaning techniques: handling missing values, outliers, and duplicates
• Using Azure Data Factory for data ingestion and transformation
• Introduction to Azure Databricks for collaborative data processing

Module 3. Fundamentals of Machine Learning

• Overview of machine learning concepts: definition, types, and lifecycle
• Introduction to supervised vs. unsupervised learning
• Key machine learning algorithms: regression, classification, and clustering
• Setting the stage for implementing machine learning on Azure

Module 4. Building Machine Learning Models on Azure

• Utilizing Azure Machine Learning Studio to build models
• Data splitting: training, validation, and test sets
• Model training and evaluation: metrics and performance assessment
• Hyperparameter tuning for improved model performance

Module 5. Deploying AI Solutions

• Introduction to deploying machine learning models on Azure
• Understanding Azure Kubernetes Service (AKS) for scalable deployment
• Creating and managing web services for AI models
• Best practices for model versioning and management

Module 6: Leveraging Azure Cognitive Services

• Overview of Azure Cognitive Services and their applications
• Implementing Computer Vision for image analysis
• Utilizing Text Analytics for sentiment analysis and language detection
• Integrating Azure’s Speech Services for voice recognition applications