The field of data science is rapidly evolving with the growing adoption of technologies to extract useful insights from massive datasets. Here are some current trends in data science that will continue, gain more momentum and possibly spawn new ones.
Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, structured or unstructured, similar to Knowledge Discovery in Database (KDD).
However, data science covers the processing of data, analysis, and artificial intelligence techniques. Data is not processed by human intelligence alone; machine intelligence has become the most popular approach in tackling this issue.
Discoveries and innovations continue to stream through cyberspace, allowing organizations to gain more insight into their data with less time and effort than ever before.
Small Data and TinyML
In data science, evolution is the Small Data TinyML. This is an emerging new concept that has affected how people do data science.
The following limitations characterize small Data: Interaction not possible, interaction with user Interaction complicated, the language of interaction limited Machine Learning effectiveness relatively low compared to Big Data.
It is suitable for those that work with cloud-based storage. TinyML has made it possible to make minute predictions with accuracy sometimes less than 1%.
Additionally, there is a Data-Driven Customer Experience, which is suitable for business interactions. The age of data business model allows businesses to decipher data science to be more competitive. It requires excellent knowledge of how to handle data management, storage, and process robotics.
One other aspect is called Data Science Assistants, has become significant. This has led to a new paradigm where Artificial Intelligence can help human beings do tasks better.
Convergence is another data science trend, which defines the interaction between traditional methods and data science.
Another trend is called Data Science in Business, where businesses use Big Data to create effective business models.
The biggest trends are Cloud-based storage, taming machine learning algorithms with human oversight, real-time data processing engines that access cloud storage at enormous speeds for fast response times.
Auto TML, which is automated machine learning, has been observed in the data trends. With this, business operations have become more efficient because several tasks are automated. However, there is a need for human oversight to ensure that the right decisions are made.
This trend has led to traditional methods with data science and helps in taming machine learning algorithms. This makes it possible for humans to be able to understand what is happening.