Is data science too hyped?

The hype around data science is only realistic for professionals who work hard, understand the core concepts and adapt a quick change. Conclusion: A high demand for skilled/expert data scientist but an oversupply of too many awareness level professionals.

Will data analytics become obsolete?

While data science jobs more or less fit that description, they probably won’t be replaced any time soon. The more likely outcome is that most lower-skilled data science jobs will be taken over by machine learning technology and higher-skilled jobs will require human attention.

Is data scientist replaced by AI?

Instead of modeling and data manipulation, the value is created in translating a client’s needs or a customer’s needs (these words have slightly different meanings but the difference isn’t important here) into some kind of model, and implementing that in a useful way. …

Is big data analytics overhyped?

However, it is evidently a technology in demand as a multitude of other technologies like the Internet of Things (IoT), content analytics, and cloud computing are influenced by big data analytics. The following are some reasons why Big Data is criticized as being overhyped: 1. Inaccuracies

Will big data fall off the hype cycle?

The recent Gartner’s Hype Cycle report for emerging technologies in 2015, published in August, 2015, has only added fuel to the fire by removing Big Data off its hype cycle. Gartner Inc., a research and advisory company, had previously predicted the fall of Big Data in 2014.

How to implement results from big data analytics effectively?

Applying experience and knowledge into insights is another way in which results from Big Data Analytics can be implemented effectively. In the long-term, it could be said that Big Data, despite being a technology that needs improvement, cannot be swept off existence.

What is the difference between predictive analytics and text analytics?

Predictive analytics does forecasting or classification by focusing on statistical or structural models while in text analytics, statistical, linguistic and structural techniques are applied to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.