Meeting the demand of data scientists

Though data analysis is not new, future applications of it will depend on advanced data scientists with analytical skills and experiences utilizing statistic models. The following events have contributed to the evolution of data science as we know the field today:

1962 John W. Tukey writes a book named The Future of Data Analysis

1974 Peter Naur writes a book named Concise Survey of Computer Methods

1989 Gregory Piatetsky-Shapiro organizes the Knowledge Discovery in Databases workshop

1996 Three professor, Usama, Gregory and Padhraic publish From Data Mining to Knowledge Discovery in Databases

1997 The first edition of Data Mining and Knowledge Discovery journal is published

2003 The first edition of Journal of Data Science is published

2007 The Research Center for Dataology and Data Science is established

2014  McKinsey & Company says that big data and advanced analytics can provide manufacture revolution.

Predictions for 2016

Scientific data will be extended across more industries and within additional business units.

A study from Accenture and GE (General Electric) shows that 89 percent of companies that do not take advantage of big data will result in a loss of market share. As a result, the data will be spread to the industrial application of science to predict energy and geopolitics.

There will be an increase in data science education programs.

According to, the number of data scientists working in the first quarter of 2015 rose 57 percent compared with last year. McKinsey & Company’s analysis forecasts massive talent shortages by 2018. The number of data science graduate programs will likely continue to increase to meet this demand.

Depth study science and technology will become indispensable data.

Deep learning can teach the system to recognize images or understanding language. It also provides multiple representations of the underlying data, and generate new ways to predict the behavior of notifications. That is why this subset of machine learning is a natural addition to the data scientist toolkit.

Scientists will use the data to learn the depth of automated feature extraction process and discover patterns in the data. Therefore, the depth of learning tools will be a turnkey solution widely used.

As databases grow, they will become larger and more sophisticated.

The amount of data in the world is expected to increase rapidly, which means that more data can be used in a broad range of disciplines.
This will create an “open data” mentality, researchers and public institutions will accelerate the learning of shared code and data. For example, in data science we can analyze public data from social media to understand the market we serve.

You can answer the growing demand for experienced data scientists by learning more about a degree in data science.