What are the Limitations of Data Science?

Data Science, AI and ML
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Despite the fact that Data Science is a profitable professional path, it is not without its pitfalls. In order to appreciate the complete picture of Data Science, we must consider its flaws. A few examples are as follows:

  • Mastering data science is nearly impossible.

Statistics, information science, and mathematics are just a few of the disciplines that make up data science. Mastering all fields and being moderately good in all of them is difficult.

  • A high level of domain awareness is required.

Data Science also has the disadvantage of being domain knowledge-dependent. An individual with extensive experience in these domains will find it difficult to handle Data Science problems without prior knowledge of Statistics and Computer Science. The same may be true in the opposite direction.

  • Data Privacy Concerns

Data is the lifeblood of many businesses, and data scientists help businesses make data-driven decisions. However, the data used in the operation may infringe on the privacy of clients. The parent business has access to user-sensitive data, which could lead to data breaches if security is exploited.