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Analytics industry is in currently under massive transformation phase. To keep up with the ever-changing needs of the kind of problems at hand, the toolkit of today’s data scientist must be equipped with cutting edge tools. One such tool, SAS, was once the swiss-knife of a Data Scientist but its inability to transform as per the modern day requirements and prohibitive pricing killed its relevance gradually and gave away its dominance to futuristic tools like Python and R. This brief article highlights some important tools that are looked upon with high reverence in today’s analytics industry.
Python: This language has been the choice of programmers for a very long time and because of its flexibility to adapt to any kind of requirement just kept increasing its importance. Be it web programming, database management, application development, Python had its role in all the fields of Software engineering. And then with the addition of analytical packages like numpy, scikit-learn, matplotlib and seaborn the data science industry received a tremendous boost as all these statistical/machine learning packages made a Data Scientist work, a breeze. Recently the integration of Deep Learning packages like Tensorflow, PyTorch has just made AI based tasks all the more feasible in Python.
R Software:Â This language has been a choice of almost all the researchers in the academic circle. Any statistician/programmer who wants to prove the capability of any new statistical model or algorithm, would get the implementation of their algorithm using R and then share their work with the rest of the world in form of R packages. The availability of a wide array of R packages provides a Data Scientist a large set of options/algorithms to help him accomplish his task at hand. Pretty much the ecosystem in Python, R also has the backing of open source community which keeps contributing in the form of better packages and enhanced functionality. R also has a wide variety of options that can be used in Machine Learning and Deep Learning applications.
Tableau: For projects that involve extracting insights from data, Tableau is the best option as it has minimal coding requirement. Even a business user can quickly extract insights out of vast swathes of data using Tableau. Tableau, however, is a proprietary product and has a licensing cost involved. But on the application side, this is the most popular choice among BI professionals and Data Scientists, all alike. Tableau is designed to work huge amounts of data and can very comfortably handle data from multiple data sources through its unique feature called Data Blending.
Orange: This is an open source data visualization software which can perform different kinds of quick data exploration and visualization tasks. This again requires absolutely zero programming knowledge and can handle visualization requirements for various machine learning algorithms as well through a predefined sequence of steps.
Auto Weka:Â In the era when Python and R are the goto tools for any data scientist, Auto Weka is a GUI driven tool wherein most of the Machine Learning tasks, Data Exploration, Data Visualization tasks can be carried out. This tool has been developed by the University of Waikato, New Zealand. This tool is again a great choice for tasks that do not involve really complex machine learning algorithms and we are probably interested in a quick turnaround with the final results and since there is no need for writing code, things can be moving pretty fast.
In case you are an aspiring data scientist or a seasoned professional in data science, it might be of interest to you to explore the above-mentioned tools and enhance your productivity by leaps and bounds
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