Edvancer's Knowledge Hub

Skills required for a megabucks career in data science

Manu Jeevan 28/05/2018

The megabucks of this era’s global digital economy are now, not in oil, but in data. Business growth is aided by leveraging data, which was not being done in the past and big data analytics; one of the most sought-after technologies has a key role to play in this regard. The digital revolution has truly revolutionized the business landscape around the world. In order to succeed in the fiercely competitive global digital economy, it is critical for any enterprise to be in possession of the lethal combination of Big Data and Analytics. To improve upon the operational efficiencies and to enable collection and storage of voluminous data, companies across the world are imbibing technologies like cloud computing, Internet of Things (IoT) and Artificial Intelligence (AI). All this, though, is to no avail to the enterprise, since the potential of analytics can be tapped only by data scientists and machine learning tools. Data scientists stand out vis-a-vis most technical professionals because of the knowledge they possess of these tools and technologies. There is an urgent need for skilled professionals in the IT sector, therefore with a huge demand for these skill-set, engineers in both the IT and non-IT job roles have a significant opportunity to have a promising and exciting career in this field. The opportunities in Data Science There is a lot to be gained for IT and non-IT professionals who make a career move to the domain of Data Science; which is fast evolving into a lucrative field, with encouraging dream payoffs. Abounding in job opportunities for engineers, Data Science is the area to plunge headlong into for both graduates and professionals. How does one go about becoming a Data Scientist? Well, it is definitely not as easy as it sounds! As there are various different skill-technology combinations involved, aspirants need to exercise great care while choosing the skills they wish to acquire. A few things must be considered when making this choice. The new skill chosen should be close to the heart, suited to the aspirant’s aptitude and should also complement the existing knowledge and expertise, in addition to being relevant to either the current field or the one that the candidate desires to switch over to. The application of Python and R is potentially enormous, particularly for enterprises in the sectors of banking, telecom, government, finance, consulting, e-commerce, insurance etc; operating in the B2C category, and are among the most sought-after skills today. In order to manage millions of Data points, organizations require adequate technological capabilities to apply algorithms in great magnitude. This is exactly what programs like R and Python allow. Take for example, a bank that wishes to strengthen its customer acquisition and retention rates. Faced with a huge volume of customer Data to be analyzed, the Data Scientist does a regression analysis on the bank’s database, using a programming language like R, to find out the customers with the highest possibility of leaving etc , while Python is a programming language preferred by most of the leading companies of the world like Google, Netflix ,IBM, Spotify and Facebook, to develop a wide range of applications working on multiple operating systems. Up-skilling your way to a career in Data Science To secure better career growth, professionals having an in-depth knowledge of programming languages like R or Python, supplemented with a database querying language like SQL, can apply for a wide range of job opportunities, considering the demand and applications that Data Analytics has in modern businesses. Regardless, a prerequisite for the candidate to capitalize on these prospects is to either re-equip themselves with new technical capabilities or up-skill to build upon their existing knowledge. What Data skills one can go for? A few of the skills an aspirant can choose are; possessing a functional knowledge of statistics, application of Machine Learning, understanding the concepts of Data Mining, Deep Learning and Artificial Intelligence, Software Programming Languages, Data Science with R, Python or SAS, and Database Management, along with Data Visualization. Depending upon the aptitude and interest of the aspirant, and of course the requirements of enterprises, these skills would enable the candidate to build a solid foundation for an extensive in-depth knowledge of a few or all of them. Skilling opportunities are available to both freshers/graduates who desire to become Data Scientists, as also to experienced professionals seeking to explore new high-growth job roles.

About
Latest Posts

Manu Jeevan

Manu Jeevan is a self-taught data scientist and loves to explain data science concepts in simple terms. You can connect with him on LinkedIn, or email him at manu@bigdataexaminer.com.