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AI & Data Science Professional
Target Audience: Software Engineers, Data Analysts, ML Engineers, Technical Consultants (1–6 years)
Duration: 80 Hours (customizable for fast-track or extended corporate formats)
Mode: Classroom | Virtual Instructor-Led | Hybrid
Learning Outcomes
- ✔Apply Python-based data analysis techniques aligned with enterprise analytics workflows
- ✔Perform data cleansing, transformation, and validation using NumPy and Pandas
- ✔Develop reproducible and maintainable analytical workflows using Jupyter Notebooks
- ✔Design, build, and evaluate Machine Learning models for real business use cases
- ✔Apply appropriate evaluation metrics aligned to business and operational KPIs
- ✔Operationalize ML models using Azure Machine Learning Studio
- ✔Integrate Azure AI Services such as Vision, Text Analytics, and Conversational AI into applications
- ✔Design and deliver end-to-end, secure, and scalable AI-powered enterprise solutions
Prerequisites
- Basic programming experience in any language
- Foundational understanding of mathematics and statistics
- Working knowledge of Git and version control practices
- Basic familiarity with REST APIs and client-server concepts
- Laptop with stable internet connectivity
Lab Setup
- Python 3.9 or later
- Jupyter Notebook / JupyterLab
- VS Code or PyCharm
- Git & GitHub
- Microsoft Azure Account (free tier sufficient)
- Azure Machine Learning Studio
- Postman
- Node.js (for Conversational AI using TypeScript)
Course Breakdown
Day 1 (8 hours):
Enterprise Data Analysis Using Python
Focus
- Python programming standards for data and analytics teams
- Control flow, functions, and modular code design
- Core data structures for analytical workflows
- Environment management and dependency control
- Effective use of Jupyter Notebooks for experimentation and reporting
Hands-on Exercises
- Develop Python scripts for structured data processing
- Explore enterprise-style datasets using Jupyter Notebooks
- Create reusable utility functions for analytics tasks
Deliverables
- Structured Python notebook demonstrating enterprise data exploration
Day 2 (8 hours):
Enterprise Data Manipulation with NumPy and Pandas
Focus
- Vectorized computation using NumPy
- DataFrames and Series for structured data processing
- Data ingestion from multiple formats (CSV, Excel, JSON)
- Filtering, aggregation, and group-based analysis
- Handling missing, inconsistent, and noisy enterprise data
Hands-on Exercises
- Clean and transform a transactional dataset
- Perform aggregations aligned with business metrics
- Prepare feature-ready datasets for ML pipelines
Deliverables
- Validated and production-ready dataset using Pandas
Day 3 (8 hours):
Data Visualization and Exploratory Analysis
Focus
- Enterprise data visualization principles
- Visualization using Matplotlib and Seaborn
- Exploratory Data Analysis (EDA) techniques
- Identifying trends, anomalies, and patterns
- Feature relevance and business interpretation
Hands-on Exercises
- Visualize operational and performance trends
- Generate insights using analytical plots
- Prepare an executive-ready EDA summary
Deliverables
- EDA report with visual insights and business interpretation
Day 4 (8 hours):
Applied Machine Learning Fundamentals
Focus
- Machine Learning lifecycle in enterprise environments
- Supervised and unsupervised learning approaches
- Linear and Logistic Regression for business problems
- Model evaluation techniques and metrics
Hands-on Exercises
- Build regression and classification models
- Evaluate models against defined KPIs
- Perform basic hyperparameter tuning
Deliverables
- Evaluated ML model with documented performance metrics
Day 5 (8 hours):
Advanced Machine Learning Techniques
Focus
- K-Means clustering for segmentation use cases
- Naive Bayes for probabilistic classification
- Feature scaling and preprocessing strategies
- Overfitting and underfitting mitigation
- Cross-validation and model robustness
Hands-on Exercises
- Customer or operational segmentation using clustering
- Classification using Naive Bayes
- Comparative model evaluation
Deliverables
- Clustering and classification models with analysis
Day 6 (8 hours):
Enterprise Machine Learning with Azure ML
Focus
- Azure ML workspace structure and governance
- Experiment tracking and model lifecycle management
- Training models at scale using Azure ML
- Model versioning and evaluation
- Secure deployment of models as REST endpoints
Hands-on Exercises
- Train ML models using Azure ML Studio
- Deploy models as secure APIs
- Consume deployed endpoints from applications
Deliverables
- Azure-deployed ML model with endpoint documentation
Day 7 (8 hours):
Azure AI Services – Vision and Text Analytics
Focus
- Azure Computer Vision capabilities
- Image analysis and OCR for enterprise use cases
- Azure Text Analytics for sentiment and insights
- AI service integration patterns in applications
Hands-on Exercises
- Develop image analysis functionality
- Analyze unstructured text data
- Integrate Azure AI services into Python applications
Deliverables
- AI-enabled application using Vision and Text services
Day 8 (8 hours):
Conversational AI and Enterprise Bots
Focus
- Conversational AI use cases in enterprises
- Azure Bot Service architecture
- QnA Maker for knowledge-based bots
- Bot development using TypeScript
- Integration with enterprise systems and AI services
Hands-on Exercises
- Build an enterprise FAQ or support bot
- Develop conversational flows using TypeScript
- Integrate NLP and AI services
Deliverables
- Enterprise-ready conversational AI bot
Day 9 (8 hours):
AI Application Integration & MLOps Foundations
Focus
- End-to-end AI solution architecture
- API-driven AI and ML integration
- Introduction to MLOps concepts
- Model monitoring and lifecycle considerations
- Overview of CI/CD for ML workflows
Hands-on Exercises
- Integrate ML models into an application
- Invoke predictions via APIs
- Design a scalable ML deployment workflow
Deliverables
- Integrated AI application with documented architecture
Day 10 (8 hours):
Capstone Project – Enterprise AI Solution
Focus
- Business problem definition and solution design
- Data preparation, modeling, and validation
- Deployment using Azure ML and AI services
- Solution evaluation and presentation
Hands-on Exercises
- Develop a complete AI-powered enterprise solution
- Deploy solution on Azure
- Present architecture, outcomes, and recommendations
Deliverables
- End-to-end enterprise AI solution
- Azure-deployed ML model
- Technical and executive-level documentation
Corporate Value-Adds
- Enterprise-aligned AI and Data Science curriculum
- Hands-on experience with Azure AI and ML platforms
- Real-world datasets and business-driven case studies
- Production-oriented AI solution design
- Flexible delivery for corporate and academic programs
Training Collateral
- Professional slide decks and reference notes
- Detailed lab manuals
- GitHub repositories with enterprise-ready code samples
- Capstone project guidelines and templates
- Assessments and knowledge checks
Return on Investment (ROI)
- ✔Accelerated AI capability development across teams
- ✔Reduced reliance on external AI vendors
- ✔Improved data-driven decision-making maturity
- ✔Faster transition from experimentation to production AI
Course Breakdown
Day 1 (8 hours):
Enterprise Data Analysis Using Python
Focus
- Python programming standards for data and analytics teams
- Control flow, functions, and modular code design
- Core data structures for analytical workflows
- Environment management and dependency control
- Effective use of Jupyter Notebooks for experimentation and reporting
Hands-on Exercises
- Develop Python scripts for structured data processing
- Explore enterprise-style datasets using Jupyter Notebooks
- Create reusable utility functions for analytics tasks
Deliverables
- Structured Python notebook demonstrating enterprise data exploration
Day 2 (8 hours):
Enterprise Data Manipulation with NumPy and Pandas
Focus
- Vectorized computation using NumPy
- DataFrames and Series for structured data processing
- Data ingestion from multiple formats (CSV, Excel, JSON)
- Filtering, aggregation, and group-based analysis
- Handling missing, inconsistent, and noisy enterprise data
Hands-on Exercises
- Clean and transform a transactional dataset
- Perform aggregations aligned with business metrics
- Prepare feature-ready datasets for ML pipelines
Deliverables
- Validated and production-ready dataset using Pandas
Day 3 (8 hours):
Data Visualization and Exploratory Analysis
Focus
- Enterprise data visualization principles
- Visualization using Matplotlib and Seaborn
- Exploratory Data Analysis (EDA) techniques
- Identifying trends, anomalies, and patterns
- Feature relevance and business interpretation
Hands-on Exercises
- Visualize operational and performance trends
- Generate insights using analytical plots
- Prepare an executive-ready EDA summary
Deliverables
- EDA report with visual insights and business interpretation
Day 4 (8 hours):
Applied Machine Learning Fundamentals
Focus
- Machine Learning lifecycle in enterprise environments
- Supervised and unsupervised learning approaches
- Linear and Logistic Regression for business problems
- Model evaluation techniques and metrics
Hands-on Exercises
- Build regression and classification models
- Evaluate models against defined KPIs
- Perform basic hyperparameter tuning
Deliverables
- Evaluated ML model with documented performance metrics
Day 5 (8 hours):
Advanced Machine Learning Techniques
Focus
- K-Means clustering for segmentation use cases
- Naive Bayes for probabilistic classification
- Feature scaling and preprocessing strategies
- Overfitting and underfitting mitigation
- Cross-validation and model robustness
Hands-on Exercises
- Customer or operational segmentation using clustering
- Classification using Naive Bayes
- Comparative model evaluation
Deliverables
- Clustering and classification models with analysis
Day 6 (8 hours):
Enterprise Machine Learning with Azure ML
Focus
- Azure ML workspace structure and governance
- Experiment tracking and model lifecycle management
- Training models at scale using Azure ML
- Model versioning and evaluation
- Secure deployment of models as REST endpoints
Hands-on Exercises
- Train ML models using Azure ML Studio
- Deploy models as secure APIs
- Consume deployed endpoints from applications
Deliverables
- Azure-deployed ML model with endpoint documentation
Day 7 (8 hours):
Azure AI Services – Vision and Text Analytics
Focus
- Azure Computer Vision capabilities
- Image analysis and OCR for enterprise use cases
- Azure Text Analytics for sentiment and insights
- AI service integration patterns in applications
Hands-on Exercises
- Develop image analysis functionality
- Analyze unstructured text data
- Integrate Azure AI services into Python applications
Deliverables
- AI-enabled application using Vision and Text services
Day 8 (8 hours):
Conversational AI and Enterprise Bots
Focus
- Conversational AI use cases in enterprises
- Azure Bot Service architecture
- QnA Maker for knowledge-based bots
- Bot development using TypeScript
- Integration with enterprise systems and AI services
Hands-on Exercises
- Build an enterprise FAQ or support bot
- Develop conversational flows using TypeScript
- Integrate NLP and AI services
Deliverables
- Enterprise-ready conversational AI bot
Day 9 (8 hours):
AI Application Integration & MLOps Foundations
Focus
- End-to-end AI solution architecture
- API-driven AI and ML integration
- Introduction to MLOps concepts
- Model monitoring and lifecycle considerations
- Overview of CI/CD for ML workflows
Hands-on Exercises
- Integrate ML models into an application
- Invoke predictions via APIs
- Design a scalable ML deployment workflow
Deliverables
- Integrated AI application with documented architecture
Day 10 (8 hours):
Capstone Project – Enterprise AI Solution
Focus
- Business problem definition and solution design
- Data preparation, modeling, and validation
- Deployment using Azure ML and AI services
- Solution evaluation and presentation
Hands-on Exercises
- Develop a complete AI-powered enterprise solution
- Deploy solution on Azure
- Present architecture, outcomes, and recommendations
Deliverables
- End-to-end enterprise AI solution
- Azure-deployed ML model
- Technical and executive-level documentation
