Data Science – Top Trends in 2016

With another year of technology advancements and Big Data gearing up at a rapid pace, the experts have been challenged to highlight their predictive inclinations towards identifying emerging trends in Data Science. Much of data science is leveraged to detect and extrapolate varying trends from incongruent data patterns. The last year has been remarkably good in the field of data science – Semantics gained more importance than ever before, the field of Big Data matured radically, and the demand for experienced data visualizers has spiked dramatically.

2016 seems to be more of the same, with the focus switching more to semantics and analytics. Hadoop has become more mainstream. These Data Science training courseswould benefit organizations seeking more comprehensive view of their data environment as well as represent lucrative opportunities for talented programmers, analysts, and architects.

The below-mentioned trends are noteworthy in 2016:

Hybrid Data Stores will Become More Common

One thing that will definitely drive the world of data science is the growth of hybrid data stores, which can store data in different ways and transform them internally. These stores also render more sophisticated and professional mid-tier kind logic. The systems may include the capability of working with RDF, JSON, XML and relational data, and possess deep query capabilities in multiple modes. The biggest advantage of hybrid data stores is that information does not need to be serialized out to text first and then back to processing. This can make such operations relatively faster.

Advent of Data Virtualization

One of the biggest trends in Data Science is the rise of data virtualization. Today, the fields of semantics, natural language queries and data hubs, all have converged into data virtualization, making Data Science Certification more effective and vital for any company. It is the process of making company data silos accessible to each other by using hybrid data systems that are capable of storing as well as retrieving content in a wide range of formats.

With the rise of data virtualization, information can come from multiple formats and channels, and can be mined for any sort of semantic attachment. The information can then be stored internally within the data system. In addition to this, data virtualization can also present outputs in a wide range of formats to aid in more sophisticated use of the data. This includes presenting it as a data stream for purposes, visualization and reporting tool.

Semantics Becomes the Buzzword

When SPARQL 1.1 was released in 2013, who knew that over a couple of years, a major upgrade to the SPARQL will emerge as the de facto mechanism to communicate with triple stores! This, in turn, has paved the path for utilizing cutting-edge semantics-based applications. Semantics gain major importance in recommendation engines and other similar applications as such systems deal more with searching and building connections between different types of resources. However, of late, experts are predicting that SPARQL Service Description language will become the latest Data Science trend 2016, offering a more standardized mechanism to discover data sets. This will make it easier for people to programmatically discover the structure of data and create tools to make the datasets easily accessible without any complexity.

Hadoop Continues to Gain Significance

There are several reasons why Hadoop is expected to drive data science even in 2016, one of the biggest reasons being companies can easily explore full datasets as well as mine larger datasets without any complexity. Hadoop also aids in large-scale data preparation and accelerates the process of data-driven innovation. Since 80% of data science is related to data preparation, the importance of Hadoop will continue to grow in years to come.