Synthetic Data for Software Testers

Course details

When real data is scarce or when privacy is paramount, using synthetic data for testing can be the solution. By artificially generating data that mimics the statistical properties of real-world data, quality assurance steps can be performed successfully and with less risk.
In this course, learn how to create such datasets and how to leverage the data during practical testing scenarios. Get introduced to algorithms and tools that can be used to create synthetic data. Receive insights regarding how to work with time-series and unstructured data, improving your confidence with handling complex data formats. A challenge in the course facilitates hands-on practice so you can start getting comfortable with the steps involved. Instructor Mike Smith also discusses the limitations associated with synthetic data, preparing you to be able navigate constraints if they arise.

Learning objectives
Identify scenarios where synthetic data could be used in testing environments
Create a synthetic dataset that mimics real-world data
Take precautionary measures to ensure data privacy during testing
Set up a synthetic data pipeline for quality assurance
Use time-series data and unstructured data during testing

Concepts

Introduction

Discover the power of synthetic data
What you should know
Who this course is for

Understanding Synthetic Data

Defining synthetic data
Generating synthetic data
Use cases for synthetic data in testing

Technical Aspects of Synthetic Data

Algorithms and tools
Ensuring data privacy with synthetic data
Testing with time-series and unstructured data

Hands-On - Generating Synthetic Data

Introduction to data generation tools
Creating your first synthetic dataset
Advanced synthetic data techniques
Hands-on project - Real-world data mimicking

Practical Implementation and Case Studies

Setting up a synthetic data pipeline
Quality assurance and synthetic data
Case study - A real-world implementation

Risks and Challenges

Limitations of synthetic data
Security concerns
Future of synthetic data and testing

Conclusion

Continue your synthetic data learning journey