Exploring Synthetic Data: The New Frontier of Data Science
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Understanding Synthetic Data
Synthetic data, simply put, refers to data that is not derived from real-world observations of the population you're interested in. In the realm of data science, the term "population" has a specific meaning, which can be explored further in related resources. Essentially, synthetic data is treated as if it originated from the desired source, although it did not.
The terminology surrounding this concept includes terms like artificial data, fake data, and simulated data, each having its own historical context. Currently, "synthetic data" has gained popularity, likely due to the need for innovation in a field that often relies on previously established concepts. However, while there are new developments, many foundational ideas remain relevant.
Let’s explore this topic further!
Infinite Possibilities
For those who have endured advanced studies in probability and measure theory, the concept of infinity can be quite familiar. It suggests that for any finite list of real numbers, new numbers can always be generated. For example, if you provide a comprehensive list of all recorded human measurements, I could still produce an entirely new number.
Where am I headed with this? Let's consider the creation of synthetic numbers. If we have a dataset containing human heights, between any two recorded heights (e.g., 173 cm and 174 cm), there exist infinite potential numbers. By extending the decimal places indefinitely, we can create values that stretch the limits of practical measurement—numbers that would be unreasonably precise.
What does this mean for generating new data points?
Real-World Data Considerations
One straightforward approach is to use actual data from real individuals. For instance, if I measure my friend Heather for your dataset, her height could serve as a valid entry—provided I adhere to the measurement standards you've established for your specific population.
However, inconsistency arises when different measurement standards are applied. If I measure Heather's height using unconventional units (like laptops) while you use millimeters, this will introduce noise into the dataset, complicating the analysis. This randomness can obscure the true data and highlights the importance of maintaining clear records regarding the data source.
When gathering real-world data, inaccuracies can easily occur. To delve deeper into this topic, consider exploring my series on data design and collection:
- The Obscure Art of Data Design
- Simple Random Sampling: Is It Actually Simple?
Handcrafted Data
What if no one is available for measurement, but you still need another data point? This is where synthetic data comes into play. If you permit synthetic entries in your project, it’s crucial to document which points are synthetic and the methods used to create them.
Alternatively, I could fabricate a height value without following any rules. For instance, I might whimsically propose a complex number just to provoke a reaction. If you set boundaries—like ensuring heights fall within realistic human ranges—I could generate plausible values like 173.5 cm or 182.4 cm.
However, these examples are influenced by my personal biases and may not accurately represent your target population. To enhance the dataset, we need to explore more robust methods in the next segment, including:
- Duplicated Data
- Resampled Data
- Bootstrapped Data
- Augmented Data
- Oversampled Data
- Simulated Data
Stay tuned for Part 2, where we will expand on these concepts!
The first video, "What is Synthetic Data? No, It's Not 'Fake' Data," delves into the fundamentals of synthetic data and its distinctions from other data types.
The second video, "Synthetic Data and Its Uses," explores various applications of synthetic data in real-world scenarios.
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