Top 8 Emerging Data Science Technologies in 2024- StarAgile

Data science in today’s time period contributes significantly to the growth of business in a competitive environment. As humans interact more and more with technology, it is impossible to keep up with all the data being generated. Data usually exists in raw form and is very much valuable to business and research. We can see a rise in demand for data scientists as per many reports. Even though the demand for data scientists is increasing rapidly, the applicants are fewer. So in upcoming years, there might be many opportunities for data scientists.

Emerging technologies in Data Science

Data science is in its initial phase so it has great potential to expand. Some of the

latest inventions and discoveries have differentiated the data science field from other major professions in many ways. The opportunities it holds can only be explained

with the help of emerging technologies which also hold a promising future.

1. Artificial Intelligence - In the study conducted by CMO, about 47% of organizations have AI in their work organizations. During the past few decades,

Artificial Intelligence and mathematics have been used to improve the interaction between technology and people. As a result of its high processing speed and data access, AI has become an integral part of our everyday lives.

In the coming years, Artificial intelligence is expected to grow exponentially due to its ability to bring innovation, provide a competitive edge to businesses, and transform how they operate. Alexa and Siri are one of the best examples, used in predictive analytics, and driverless cars.

Also read an article on Data Science vs Artificial Intelligence

2. Cloud Services - In the current time period globally a huge amount of valuable data is generated every second, so cloud computing and services tackle this problem efficiently by storing all the data at a low cost.

3. AR/VR Systems - Augmented Reality (AR) and Virtual reality (VR) together with the aid of machine learning and Natural Language Processing (NLP), automate data insights and enhance human-machine interaction. They are already catching the attention of individuals and businesses throughout the world. According to the study conducted by eMarketer about 49 million people use VR, whereas 68.7 million people are users of AR.

4. IoT - IOT also known as the Internet of things is made up of a number of objects that share an IP address and an internet connection. They also communicate among themselves via the internet. Currently, IoT has become a boon with its sensor technology and smart meters, and data scientists are also working on this technology to move to the next level by using IOT for predictive analysis. A current study conducted by Fortune Business Insights shows that the IOT devices will reach $1.1 trillion by 2026.

Also Read: What is Data Mining?

5. Big Data - Data sets that are too large to process quickly with traditional techniques have contributed to the development of big data, which refers to large data sets that can either be structured or unstructured. Advanced techniques are to be used in this case. If we properly handle and analyze the big data then we can even have access to dark data migration, strong cybersecurity and smart bots.

A recent study by Big Data Made Simple shows that in the recent two years, about 90% of the world's data has been created when compared to a long period of time. In the coming years, Big Data will change the entire outlook of the business and customers interact with the advanced technology

6. Automated Machine Learning - In today’s time period it is commonly known as AutoML and is also used to develop advanced models for Machine learning. Data engineers, researchers, and data scientists, who have been lacking in supply, will be supplemented by automation of this data. Several companies like Facebook already have Automated Machine Learning at its service.

AutoML is used to increase the efficiency of the prediction and tune the algorithms used. So, the time required to fix the workflow can be saved with AutoML, thus the scientists can focus more on the complex problems.

7. Quantum Computing - There is a lot of room for quantum computing, but it is still in its infant stage. The technology at the moment is able to perform complex calculations within a fraction of a second, which would have taken modern computers over a hundred years to solve. Large companies like Google have already begun researching quantum computing, which involves storing a large amount of data in quantum bits or qubits.

8. Digital Twins - Based on the concept that the physical objects in the real world must exist in the digital world, the digital twin trend aims to create virtual versions of

physical elements. With this, the data scientists will be able to simulate a device or system before implementing it to fully understand its pros and cons.

Conclusion

With the help of the advanced technology of AI and quantum computing, we can do many miracles. The best part is that this tech is not confined, infact data scientists and business owners can use this technology alike. Businesses that adopt machine learning will be able to enhance their business success and customer satisfaction as they interact with technology.Enroll in StarAgile’s data science online course to learn how to implement these advance technology with data science skill.

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