What you'll learn

Understand the concept, importance, and benefits of synthetic data

Apply different techniques and tools for synthetic data generation, such as decision trees, deep learning techniques, and iterative proportional fitting

Measure and compare the quality and utility of synthetic data, using various metrics and criteria, such as statistical similarity

Explore some real-world use cases and examples of synthetic data in various domains, such as healthcare, finance, e-commerce, and social media

Requirements

No programming experience is required for this course though background knowledge in Machine Learning would be helpful.

Description

Do you want to learn how to generate and use synthetic data for your business needs, such as testing, training, research, or analysis, without violating the privacy or confidentiality of the real data owners or subjects? Do you want to explore different techniques and tools for synthetic data generation, such as decision trees, deep learning techniques, and iterative proportional fitting? Do you want to discover some real-world use cases and examples of synthetic data in various domains, such as healthcare, finance, e-commerce, and social media? If yes, then this course is for you.In this course, you will learn what synthetic data is, how to generate it, how to evaluate it, and how to use it effectively and efficiently in your business. You will also learn some best practices and tips for synthetic data generation and use, and some ethical and legal issues and challenges of synthetic data use. By the end of this course, you will be able to:Understand the concept, importance, and benefits of synthetic dataApply different techniques and tools for synthetic data generation, such as decision trees, deep learning techniques, and iterative proportional fittingMeasure and compare the quality and utility of synthetic data, using various metrics and criteria, such as statistical similarity, privacy preservation, and data utilityFollow some best practices and tips for synthetic data generation and use, such as working with clean data, assessing the similarity and utility of synthetic data, and outsourcing support if necessaryExplore some real-world use cases and examples of synthetic data in various domains, such as healthcare, finance, e-commerce, and social mediaDiscuss some ethical and legal issues and challenges of synthetic data use, such as data ownership, consent, and governanceThis course is designed for anyone who is interested in learning about synthetic data, especially for business purposes. You do not need any prior knowledge or experience with synthetic data, but you should have some basic understanding of data analysis and statistics. You should also have access to a computer with an internet connection, and some software tools that we will use in this course, such as Gretel, Synthpop, and SDV.