Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Generative AI for Synthetic Data Modelling with Python SDV

Name: Generative AI for Synthetic Data Modelling with Python SDV
Rating: 3.6 (18 reviews)

Generating Synthetic Data with GenAI tools and Python: Techniques, Model Selection, and Real-World Applications

Created byLucas Whitaker

Last updated 6/2024

English

English [Auto],

What you'll learn

Master Python techniques for synthetic data generation with SDV.
Understand the importance and applications of synthetic data.
Generate high-quality synthetic data using GANs and VAEs.
Preprocess real-world data for effective synthetic data modeling.
Select and implement the best models for synthetic data generation.
Evaluate synthetic data quality with SDMetrics.
Ensure data privacy and integrity in synthetic data generation.
Apply synthetic data techniques to healthcare, finance, and retail.
Handle complex datasets with advanced synthetic data techniques.
Explore future trends and technologies in synthetic data generation.

Course content

5 sections • 25 lectures • 2h 14m total length

Introduction to Synthetic Data8:35
Generate a synthetic data set that replicates the statistical properties of real data through algorithms and simulations, enabling privacy-preserving research and robust machine learning development.
Overview of Synthetic Data Generation8:55
Explore methods for synthetic data generation, including statistical methods, generative models like GANs and VAEs, and rule-based generation, while weighing their strengths, limitations, and applications.
Introduction to SDV (Synthetic Data Vault)6:59
Explore the SDV library for generating high-quality synthetic data that mirrors real data while preserving privacy; model tabular, relational, and time series data with probabilistic graphical models and deep learning.
Environment Setup1:30
Introduction to Synthetic Data Quiz

SDV Core Concepts6:15
Learn the SDV library's core concepts of synthetic data, including data modeling with probabilistic graphical models and deep learning, to generate realistic tabular, relational, and time series data.
Getting Started with SDV5:17
Explore how the SDV ecosystem generates synthetic data that preserves real data properties through a workflow of data preprocessing, model selection, data generation, and evaluation.
Basics of SDV Quiz
Data Preparation for SDV5:49
Prepare real world data for SDV through cleaning, filtering, transforming, and handling missing values and outliers, then encode and scale different data types for accurate synthetic data modelling.
Model Selection in SDV7:21
Choose the right SDV model by considering data complexity and scalability, using Gaussian copula, CTGAN, and TV for mixed data types, and evaluate with Jensen-Shannon divergence and chi-squared tests.
Model Selection in SDV Quiz
Basic of SDV Review2:12

Introduction to Tabular Data in SDV8:15
Explore tabular data fundamentals in SDV, including the two-dimensional structure, categorical and numerical variables, common challenges like missing values and outliers, and how to model relationships with synthetic data generation.
Fitting Models to Tabular Data6:27
Explore fitting models to tabular data using maximum likelihood estimation, Bayesian inference, and neural networks, including data preparation, model selection, and interpretability for synthetic data generation.
Generating Synthetic Tabular Data (Step by Step)3:14
Advanced Techniques with Tabular Data7:42
Explore advanced techniques for tabular data in SDV, including feature selection, dimensionality reduction, missing and sparse data handling, and fine-tuning models with domain knowledge.
Tabular Data Generation Quiz
Tabular Data Review4:19

Introduction to Relational Data5:18
Explore relational databases with entities, relationships, and primary and foreign keys, and see how SDV models these relationships to generate synthetic data while preserving referential integrity.
SDV Features for Relational Data4:23
Explore how SDV uses Gaussian copula and generative models, including conditional tabular GAN and variational autoencoders, to model relational data, preserve referential integrity across multi-table structures, and handle missing data.
Generating Relational Data (Step by Step)4:00
Case Study: Relational Data2:14
Case study on relational data shows how the synthetic data vault preserves privacy in finance while maintaining relational integrity, using Gaussian copula and conditional tabular GAN.
Relational Data Quiz
Relational Data Review4:32

Importance of Data Validation5:13
Evaluate and validate the quality of generated synthetic data using SDV tools and SD metric library, compare distributions and correlations to real data, and visualize results for reliable model training.
Evaluating Synthetic Data with SDMetrics5:14
Assess synthetic data quality using SD metrics to compare real and synthetic datasets across statistical properties, integrity, fidelity, and privacy, aided by open source tools and actionable reports and visualizations.
Practical Evaluation Techniques6:11
Learn practical evaluation techniques for validating synthetic data with SD metrics, using quality and diagnostic reports, and assess mean, variance, skewness, kurtosis, correlation similarity, and Kullback-Leibler divergence and Wasserstein distance.
Evaluating Synthetic Data Quiz
Using SDMetric Demonstration2:41
Improving Synthetic Data Quality7:29
Identify and fix biases, inaccuracies, and missing representations to improve synthetic data quality. Use SD metrics and visual diagnostics to guide model tuning and post-processing for reliable, ethical synthetic data.
Evaluating Synthetic Data Review4:29

Requirements

Basic Programming Knowledge: Familiarity with Python programming is recommended.
Fundamental Data Science Concepts: A basic understanding of data science principles is recommended.
oftware Requirements: Learners should have access to a computer with an internet connection and the ability to install necessary software packages such as Python, NumPy, Pandas, and SDV.
Interest in Data Analysis: Enthusiasm for data analysis and a willingness to learn about synthetic data generation and its applications across various industries.
No advanced programming experience or deep knowledge of machine learning is required. This course is designed to guide you through all necessary concepts and tools from the ground up, making it accessible to beginners eager to explore synthetic data generation.

Description

Unlock the potential of your data with our course "Practical Synthetic Data Generation with Python SDV & GenAI". Designed for researchers, data scientists, and machine learning enthusiasts, this course will guide you through the essentials of synthetic data generation using the powerful Synthetic Data Vault (SDV) library in Python.

Why Synthetic Data?

In today's data-driven world, synthetic data offers a revolutionary way to overcome challenges related to data privacy, scarcity, and bias. Synthetic data mimics the statistical properties of real-world data, providing a versatile solution for enhancing machine learning models, conducting research, and performing data analysis without compromising sensitive information.

Why Synthetic Data?

In today's data-driven world, synthetic data offers a revolutionary way to overcome challenges related to data privacy, scarcity, and bias. Synthetic data mimics the statistical properties of real-world data, providing a versatile solution for enhancing machine learning models, conducting data analysis, and performing research and development (R&D) without compromising sensitive information.

What You'll Learn

Module 1: Introduction to Synthetic Data and SDV

Introduction to Synthetic Data: Understand what synthetic data is and its significance in various domains. Learn how it can augment datasets, preserve privacy, and address data scarcity.
Methods and Techniques: Explore different approaches for generating synthetic data, from statistical methods to advanced generative models like GANs and VAEs.
Overview of SDV: Dive into the SDV library, its architecture, functionalities, and supported data types. Discover why SDV is a preferred tool for synthetic data generation.

Module 2: Understanding the Basics of SDV

SDV Core Concepts: Grasp the fundamental terms and concepts related to SDV, including data modeling and generation techniques.
Getting Started with SDV: Learn the typical workflow of using SDV, from data preprocessing to model selection and data generation.
Data Preparation: Gain insights into preparing real-world data for SDV, addressing common issues like missing values and data normalization.

Module 3: Working with Tabular Data

Introduction to Tabular Data: Understand the structure and characteristics of tabular data and key considerations for working with it.
Model Fitting and Data Generation: Learn the process of fitting models to tabular data and generating high-quality synthetic datasets.

Module 4: Working with Relational Data

Introduction to Relational Data: Discover the complexities of relational databases and how to handle them with SDV.
SDV Features for Relational Data: Explore SDV’s tailored features for modeling and generating relational data.
Practical Data Generation: Follow step-by-step instructions for generating synthetic data while maintaining data integrity and consistency.

Module 5: Evaluation and Validation of Synthetic Data

Importance of Data Validation: Understand why validating synthetic data is crucial for ensuring its reliability and usability.
Evaluating Synthetic Data with SDMetrics: Learn how to use SDMetrics for assessing the quality of synthetic data with key metrics.
Improving Data Quality: Discover strategies for identifying and fixing common issues in synthetic data, ensuring it meets high-quality standards.

Why Enroll?

This course provides a unique blend of theoretical knowledge and practical skills, empowering you to harness the full potential of synthetic data. Whether you're a seasoned professional or a beginner, our step-by-step guidance, real-world examples, and hands-on exercises will enhance your expertise and confidence in using SDV.

Enroll today and transform your data handling capabilities with the cutting-edge techniques of synthetic data generation, data analysis, and machine learning!

Who this course is for:

Professionals looking to enhance their skills in data generation, model training, and data augmentation.
Individuals working with machine learning models who need high-quality synthetic data for training, testing, and validating their algorithms.
Scholars conducting research in fields such as healthcare, finance, and social sciences who require synthetic data to ensure privacy and compliance with ethical standards.
Developers interested in incorporating synthetic data generation into their applications, particularly those working on projects that involve data privacy, data sharing, and compliance with regulatory requirements.
Business analysts and decision-makers seeking to understand the potential of synthetic data in driving business insights, improving decision-making processes, and maintaining data privacy.
Learners and enthusiasts with a basic understanding of programming and data science who are curious about synthetic data generation and its real-world applications. This course offers an entry point to explore this growing field.

Generative AI for Synthetic Data Modelling with Python SDV

What you'll learn

Explore related topics

Course content

Introduction to Synthetic Data and SDV4 lectures • 26min

Understanding the Basics of SDV5 lectures • 27min

Working with Tabular Data5 lectures • 30min

Working with Relational Data5 lectures • 20min

Evaluation and Validation of Synthetic Data6 lectures • 31min

Requirements

Description

Who this course is for: