Data analysis always gives ultimate result in some definite terms. Different methods, resources, and methods can help in information dissection, developing it into workable insights.
Big Data analysis systems:
Open source systems like R, with its rapid mature ecosystem and Python, with its scikit-learn libraries and pandas; appear stand for continuing their control over the analytics space. Particularly, some projects in the Python ecosystem appear mature for fast adoption: Big data  foundation and practitioner course with most experienced professionals help you to grow your career.
Anaconda python:
These days, data scientists work with lots of data sources, ranging from SQL databases and CSV files to Apache Hadoop clusters. The expression engine of blaze helps data scientists utilize a constant API for working with a complete range of data sources, brightening the cognitive load needed by utilization of different systems.
Of course, Python and R ecosystems are just the beginning, for the Apache Spark system is also appearing increasing adoption – not least as it provides APIs in R and also in Python.
Launching on a usual trend of utilizing open source networks, we can also predict for seeing a move towards the approaches based on distribution. For example, Anaconda Python provides an equal distribution for both R and Python, and Canopy provides only a Python distribution suited for data science. And no one will be surprised if they see the integration of analytics software like python or R in a common database.
Beyond open source systems, a developing body of tools also helps business users communicate with prince2 foundation and practitioner course directly while helps them form guided data analysis and project management. These tools attempt for abstracting the data science procedure away from the user. Though this approach is still immature, it provides what seems for being a very potential system for data analysis.
Going forward, we expect that tools of data and analytics will see the rapid application in mainstream business procedures, and we anticipate this use for guiding companies towards a data-driven approach for making decisions. For now, we need to keep our eyes on the previous tools, as we don’t want to miss seeing how they reshape the data’s world.
So, encounter the strength of Apache Spark in an integrated growth ambiance for data science. Also, experience the data science by joining data science certification training course for exploring how both R and Spark can be used for building the applications of your own data science. So, this was the complete overview on the top tools and technologies which dominate the analytics space in 2016.
The author is a famous data analyst who writes blogs and articles to deliver the benefits of data science certification training course in India.