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Synthetic Data: How To Use It and Generate It
Rating: 3.4 out of 5(6 ratings)
64 students

Synthetic Data: How To Use It and Generate It

Everything From The Programs To Use To The Types of Data You Can Create!
Created byRichard Aragon
Last updated 1/2024
English
English [Auto],

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

Course content

1 section • 9 lectures • 1h 19m total length

  • Introduction3:14

    Understand what synthetic data is, how to generate and evaluate it, and how to apply it in business using tools like Gretl, synthpop, and SDV.

  • Synthetic Data Using Decision Trees10:13

    Define synthetic data and its fully, partially, and hybrid types, and outline its testing, training, and research uses along with privacy, diversity, and ethics challenges; preview decision tree techniques.

  • Synthetic Data Generation Using Deep Learning Techniques5:45

    Explore synthetic data generation using decision trees, deep learning, and iterative proportional filling; compare advantages and disadvantages, and use Gretel, synthpop, and SDV.

  • Synthetic Data Generation Using Iterative Proportional Fitting5:57

    Leverage deep learning techniques, including GANs and VAEs, to generate high-fidelity synthetic data across images, text, audio, and video, and compare advantages and disadvantages using Gretel, synthpop, and SDV.

  • Measuring and Comparing Synthetic Data Quality and Utility5:43

    Introduce the iterative proportional fitting technique (IPF) for synthetic data generation, detailing steps to match marginal totals and evaluating advantages and disadvantages; demonstrate using synthpop and Stevie.

  • Synthetic Data Lecture 67:34

    Explore how to measure and compare synthetic data quality and utility using t closeness, differential privacy, and Synthetic Data Vault, focusing on privacy preservation, statistical similarity, and task performance.

  • Synthetic Data Lecture 716:09

    Apply best practices for synthetic data generation and use, including clean data, assessing similarity and utility, and considering ethics, ownership, consent, and governance in real-world domains.

  • Synthetic Data Hands On and Practical Lecture18:17

    Learn hands-on approaches to generate synthetic data using RNN and GAN methods, with practical steps in Colab, Gretl tools, and Hugging Face for data sets.

  • Airoboros and Synthesizer for Synthetic Data6:51

    Explore synthetic data creation with synthesizer and Ouroboros Burrows, featuring simple pip installation, Apache 2.0 licensing, API key setup, and prompt-driven generation with flexible models.

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 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, privacy preservation, and data utility

  • Follow 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 necessary

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

  • Discuss some ethical and legal issues and challenges of synthetic data use, such as data ownership, consent, and governance

This 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.

Who this course is for:

  • Anyone who is looking to learn more about synthetic data creation, especially for corporate use.

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