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4 basic skills of a data scientist

This article looks at the various skills required by a data scientist in his day to day business. The article adopts an approach of grouping various skills into 4 different sets. The skill of data mining analyses how to extract useful data. The skill of data analytics takes care of how data should be analyzed. Data management deals with various modes to manage huge chunks of data. Data visualization is involved in skillfully portraying the data and depicting it in a more comprehensible form.
The skill of data mining

Let us take data mining first. Data mining is all about digging out useful information from large sets of unstructured data. For instance, mining a list of students who scored more than 90 percent in an exam should be helpful while preparing an internship list on the basis of merit. So, the skill of data mining forms the foremost tool which a data scientist should possess.

The skill of data analytics

Data analytics is the art of organizing data in a form in which it can be analyzed for further processing. This means that we should first tabulate our data using tools like excel to complete the second step in the processing. Though arranging small quantities of data may be easier but when the given data is enormously undistributed, excel training may be required. For instance, excel training in Malaysia is being provided by numerous institutions and these institutions are playing a leading role in data analytics.

The skill of data management

Data management is the skill of managing data. Let us extrapolate the above example to explain data management. When we talked about excel training in Malaysia, our prime focus was on analytics. In management, the major focus is on computation and processing so that visualization operations can be performed better.

The skill of data visualization

Data visualization can be done in various ways. We may take the help of heat maps, polar diagrams, pie charts, bar graphics and even statistical tools like histogram.
What we are really concerned with here is that the final data should now be presented in the form of meaningful information in the best way possible.

Concluding remarks

The skills of a data scientist mentioned above are comprehensive but more can be added to these in the long run. In one word, the skill set of a data scientist keeps on expanding as technological revolution continues to advance to higher levels.
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