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Top tools and technologies to master analytics in 2016

Data analysis always gives the final result in some defined terms. Different techniques, tools and procedures can help in the dissection of data, transforming it into actionable information. Looking to the future of data analytics, we can predict some of the latest trends in technologies and tools being used to dominate the analytics space:

1. Implementation systems model

2. Display systems

3. Data analysis systems

1. Implementation systems model:

Several service providers want to replicate the SaaS model on premises, especially the following:

-Open CPU

-Yeah

– Domino Data Labs

In addition, by requiring the implementation of models, there is also a growing requirement to document code. At the same time, one might expect to see a version control system that is suitable for data science, providing the ability to track multiple versions of data sets.

2. Display systems:

Visualizations are about to be dominated by the uses of web techniques such as JavaScript systems. Basically, everyone wants to do dynamic visualizations, however, not everyone is a web developer, or not everyone has the time to spend writing JavaScript code. Naturally, some systems have been rapidly gaining popularity:

Bokeh:

This library may be limited to Python only, however it also provides a strong chance for rapid adoption in the future.

Plot:

By providing APIs in Matlab, R, and Python, this data visualization tool has been making a name for itself and seems to be on its way to rapid and wide adoption.

Also, these 2 examples are just the beginning. We should expect to see JavaScript-based systems that provide constant Python and R APIs to evolve as they see rapid adoption.

3. Data analysis systems:

Open source systems like R, with its rapidly mature ecosystem, and Python, with its scikit-learn and pandas libraries; they seem to represent the continuation of his control over the analytic space. In particular, some projects in the Python ecosystem seem ripe for rapid adoption:

Bcolz:

By providing the ability to perform processing on disk rather than in memory, this exciting project aims to find a middle ground between using local devices for in-memory computations and using Hadoop for cluster processing. , thus providing a ready solution while the data size is very small. you need a Hadoop cluster but not as small as if it was managed in-memory.

Radiance:

Data scientists today work with many data sources, from SQL databases and CSV files to Apache Hadoop clusters. The blaze expression engine helps data scientists use a consistent API to work with a full range of data sources, improving the cognitive load required to utilize different systems.

Of course, the Python and R ecosystems are just the beginning, as the Apache Spark system is also increasing in adoption, not least because it provides APIs in R as well as Python.

Establishing a regular trend of using open source ecosystems, we can also predict a move towards distribution-based approaches. For example, Anaconda provides distributions for R and Python, and Canopy only provides a Python distribution suitable for data science. And no one will be surprised if they see the integration of analytics software like Python or R into a common database.

Beyond open source systems, a body of tools in development also helps business users communicate with data directly while helping them perform guided data analysis. These tools try to abstract the data science procedure from the user. Although this approach is still immature, it provides what appears to be a very potential system for data analysis.

In the future, we expect data and analytics tools to be rapidly applied into core business processes, and we anticipate this use to guide companies toward a data-driven approach to decision making. For now, we should keep our eyes on the above tools, as we don’t want to miss seeing how they reshape the world of data.

So find the strength of Apache Spark in an integrated growth environment for data science. Also, experience data science by joining the data science certification training course to explore how R and Spark can be used to build your own data science applications. So, this was the full overview of the top tools and technologies dominating the analytics space in 2016.

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