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Top Skills Recruiters Vouch for in Data Science


By NIIT Editorial

Published on 05/03/2021

5 minutes

A phenomenon that was arguably started by a Harvard Business article that dubbed data science as the sexiest job of the 21st century has led to millions of people upskilling to avail the potential benefits. It is a very demanding domain, one that continues to show uphill growth. If studies are to be believed, it is expected that by 2026, both data science and data analytics would combinedly have up to 11 million jobs. But who would these jobs be most suited to, or, were we to phrase it otherwise, what skills would be required to buy an entry ticket into data science. Let us find out! 

 

Skills Required for Data Science 

 

The following are some technical skills expected of data science professionals. 

 

SAS

 

This is one software that has an established monopoly when it comes to data analytics software. Having developed in the mid-70s, SAS has gone through various iterations and offers a whole range of specifications that allow users to manage data, perform business and predictive analysis, as well as carry out multivariate analysis if need be. As per its parent company, SAS is officially subscribed by 83,000 plus users around the world that includes government organizations, private enterprises, and academic institutions. 

 

Python 

This programming language has burst forth in recent years as an alternative to high-volume coding languages. Python is easy to code, debug, modify, and majorly deployed for applications in predictive analytics. Apart from building mathematical models, developers can create beautiful front ends with Python. Being a dynamically typed language, coders do not have to specify the variable type in advance, a feature that makes the code minimal and efficient. 
 

SQL  

 

SQL is used to write queries in databases and makes the task of extracting meaningful insights simpler. It is cross-compatible with databases such as MS Access, MySQL, SAP HANA, and Oracle Databases to name a few. The Structured Query Language allows easy modifications in pre-existing tables and hassle-free database scalability options. In addition to that, it is considered highly secure to use SQL as admins can define relevant permissions for data modules. 

 

R Programming Language 

 

R is symbolic of statistical analysis with applications in data science. It is a free software environment that is used regularly by data miners across the world. The R environment is open source and is cross-compatible with other programming languages such as Python, Java, C/C++, etc. The expert opinion holds that R is highly efficient to create recyclable statistical analysis. 

 

Hadoop 

 

This software framework was engineered by the Apache Software Foundation primarily for processing big data. It has 3 major components namely MapReduce, HDFS (High Definition File System), and YARN. Hadoop offers a high level of fault tolerance, data reliability, data localization, and data reliability. In consideration of such features, it is cost-effective and relatively easy to use. One of the USPs of Hadoop is that it stores data in a segmented fashion which in turn opens up the option to process the same on different nodes. In simple words, managing data on Hadoop is lightning fast. 

 

Rounding Up

 

This is just the tip of the iceberg. Data science requires a long, evolving checklist of skills that individuals must keep up with to maintain professional relevance. Programming languages like python and R lend a helping hand to beginners, but as they deep dive into subjects, machine learning, deep learning, data analysis and predictive modelling begin to unravel. 

 

It is mission-critical that learners with varying levels of knowledge, be it a beginner, an intermediate, or advanced level find relevant learning paths to compliment their aptitude. The following StackRoute programs are designed to deliver exactly that:

 

 

Apply now and initiate a sure-short route to become a data science professional.



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