What exactly is a data science consultant

Frequently asked questions (FAQ) about data science

There is already increasing automation in data science, and the pace of that automation is certainly not going to slow. A data scientist can already program a machine for the automated grid search of all possible combinations of thousands of data parameters in order to find the best possible solution to a specific problem in real time.

In the past statisticians had to manually design their predictive models and readjust them over and over again, using a combination of statistical experience and human creativity. Growing amounts of data and increasingly complex business problems make this type of task so mathematically complex that it cannot be mastered without artificial intelligence, machine learning and automation. In view of the steadily swelling flood of big data, this trend is likely to continue unchecked.

While AI and ML are often linked to the elimination of human labor, in fact they actually add to the importance of data scientists and related professions. In order to achieve a competitive advantage even when all companies have access to these technologies, continuous innovations and innovative approaches are required that constantly explore the current limits of statistics, IT and specialist knowledge. It is the job of data scientists to provide new theories, R&D approaches and ad hoc applications of AI that enable the next generation of strategic and financial results.

There is currently nothing to suggest that automation will replace skilled data scientists, data engineers, and DataOps specialists such as those at Hitachi. Too much human creativity is required in the various steps to fully unlock the potential of automation and AI.