15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
- Data engineers
- Business intelligence professionals
- Statisticians
- Quantitative analysts
- Research scientists
- Predictive analytics professionals
- Data science managers
- Analytics managers
- AI and machine learning researchers
- Data architects
- MLOps professionals
- Technology and digital transformation leaders
- Professionals responsible for developing AI and data-driven solutions
Session Objectives
- Understand the fundamentals of generative AI for data science.
- Master generative adversarial networks (GANs) for synthetic data generation.
- Utilize variational autoencoders (VAEs) for latent space modeling.
- Implement large language models (LLMs) for data augmentation and analysis.
- Design and build generative AI models for data science tasks.
- Optimize generative AI models for performance and efficiency.
- Troubleshoot and address common challenges in generative AI applications.
- Implement generative AI techniques for data augmentation and synthetic data generation.
- Integrate generative AI with real-world data science workflows.
- Understand how to handle ethical considerations and biases in generative AI.
- Explore advanced generative AI techniques (e.g., diffusion models, transformer-based generators).
- Apply real world use cases for generative AI in data science.
- Leverage generative AI libraries and tools for efficient development.
About the Course
This advanced training equips data science professionals with the knowledge and practical skills to integrate Generative AI into data science workflows and leverage synthetic data for analytics, modelling, experimentation, and AI development.
Participants will explore how large language models (LLMs) and generative AI tools can accelerate data preparation, exploratory analysis, feature engineering, code generation, statistical analysis, model development, documentation, and analytical reporting. The course also examines synthetic data generation techniques for addressing data scarcity, privacy concerns, testing requirements, and machine learning development.Through practical applications, participants will learn how to combine traditional data science methods with Generative AI while maintaining analytical accuracy, data quality, privacy, security, and responsible AI practices. The course emphasizes effective human-AI collaboration rather than replacing established statistical and machine learning methodologies.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of generative AI for data science.
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Workshop 1.2:
Overview of GANs, VAEs, and LLMs.
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Workshop 1.3:
Setting up a generative AI development environment.
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Workshop 1.4:
Introduction to generative AI libraries and tools.
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Workshop 1.5:
Best practices for generative AI in data science.
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Workshop 2.1:
Implementing GANs for synthetic data generation.
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Workshop 2.2:
Utilizing different GAN architectures (DCGAN, StyleGAN).
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Workshop 2.3:
Designing and building GAN models for specific data types.
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Workshop 2.4:
Optimizing GAN training and evaluation.
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Workshop 2.5:
Best practices for GANs.
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Workshop 3.1:
Implementing VAEs for latent space modeling.
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Workshop 3.2:
Utilizing VAEs for data generation and anomaly detection.
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Workshop 3.3:
Designing and building VAE models for data exploration.
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Workshop 3.4:
Optimizing VAEs for latent space representation.
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Workshop 3.5:
Best practices for VAEs.
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Workshop 4.1:
Implementing LLMs for data augmentation and analysis.
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Workshop 4.2:
Utilizing transformer-based LLMs (GPT, BERT).
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Workshop 4.3:
Designing and building LLM-based data processing pipelines.
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Workshop 4.4:
Optimizing LLMs for natural language data tasks.
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Workshop 4.5:
Best practices for LLMs.
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Workshop 5.1:
Designing and building generative AI models for data science tasks.
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Workshop 5.2:
Implementing generative AI for data imputation and anomaly detection.
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Workshop 5.3:
Utilizing generative AI for feature generation and data synthesis.
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Workshop 5.4:
Optimizing models for specific data science applications.
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Workshop 5.5:
Best practices for model design.
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Workshop 6.1:
Optimizing generative AI models for performance and efficiency.
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Workshop 6.2:
Utilizing hyperparameter tuning and model compression.
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Workshop 6.3:
Implementing distributed training and inference.
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Workshop 6.4:
Designing scalable generative AI solutions.
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Workshop 6.5:
Best practices for model optimization.
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Workshop 7.1:
Debugging common challenges in generative AI applications.
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Workshop 7.2:
Analyzing model performance and stability.
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Workshop 7.3:
Utilizing troubleshooting techniques for problem resolution.
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Workshop 7.4:
Resolving common generative AI issues.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing generative AI techniques for data augmentation.
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Workshop 8.2:
Utilizing GANs and VAEs for creating synthetic data.
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Workshop 8.3:
Designing and building data augmentation pipelines.
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Workshop 8.4:
Optimizing augmentation for model training.
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Workshop 8.5:
Best practices for data augmentation.
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Workshop 9.1:
Integrating generative AI with real-world data science workflows.
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Workshop 9.2:
Utilizing APIs and deployment tools.
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Workshop 9.3:
Implementing generative AI in data processing and model training.
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Workshop 9.4:
Optimizing integration for business impact.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Implementing ethical considerations and bias mitigation in generative AI.
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Workshop 10.2:
Utilizing fairness metrics and bias detection techniques.
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Workshop 10.3:
Designing and building ethical generative AI applications.
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Workshop 10.4:
Optimizing data handling for ethical compliance.
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Workshop 10.5:
Best practices for ethics.
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Workshop 11.1:
Exploring advanced generative AI techniques (diffusion models, transformer-based generators).
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Workshop 11.2:
Utilizing diffusion models for high-quality data generation.
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Workshop 11.3:
Implementing transformer-based generators for sequential data.
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Workshop 11.4:
Designing and building advanced generative AI solutions.
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Workshop 11.5:
Optimizing advanced techniques for specific applications.
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Workshop 11.6:
Best practices for advanced techniques.
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Workshop 12.1:
Implementing generative AI for synthetic medical data generation.
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Workshop 12.2:
Utilizing generative AI for financial fraud detection.
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Workshop 12.3:
Implementing generative AI for image and video synthesis.
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Workshop 12.4:
Utilizing generative AI for text data augmentation and summarization.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing TensorFlow, PyTorch, and Hugging Face Transformers.
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Workshop 13.2:
Implementing generative AI models with libraries.
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Workshop 13.3:
Designing and building generative AI pipelines.
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Workshop 13.4:
Optimizing library usage for efficient development.
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Workshop 13.5:
Best practices for library implementation.
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Workshop 14.1:
Implementing model evaluation and monitoring for generative AI.
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Workshop 14.2:
Utilizing evaluation metrics and visualization tools.
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Workshop 14.3:
Designing and building monitoring dashboards.
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Workshop 14.4:
Optimizing monitoring for proactive issue detection.
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Workshop 14.5:
Best practices for monitoring.
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Workshop 15.1:
Emerging trends in generative AI for data science.
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Workshop 15.2:
Utilizing multimodal generative AI models.
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Workshop 15.3:
Implementing generative AI for automated data analysis.
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Workshop 15.4:
Best practices for future applications.
