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

0. Introduction

01 - Discover the power of synthetic data
02 - What you should know
03 - Who this course is for

1. Understanding Synthetic Data

04 - Defining synthetic data
05 - Generating synthetic data
06 - Use cases for synthetic data in testing

2. Technical Aspects of Synthetic Data

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

3. Hands-On - Generating Synthetic Data

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

4. Practical Implementation and Case Studies

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

5. Risks and Challenges

17 - Limitations of synthetic data
18 - Security concerns
19 - Future of synthetic data and testing

Conclusion

20 - Continue your synthetic data learning journey

Synthetic Data for Software Testers

Authors

Mike Smith