Data rightly called the new oil of the 21st-century global digital economy, is an extremely important currency with great intrinsic value. The digital revolution has forever changed the business landscape around the world and big data analytics has emerged as one of the most sought-after technologies that enable businesses to leverage data, hitherto largely unused, to aid business growth.
Big data and analytics together make a formidable combination and any enterprise possessing these capabilities truly has the potential to succeed in this intensely competitive business landscape. Moreover, technologies like AI, Internet of Things (IoT) and cloud computing are being adopted by businesses across the globe to enhance operational efficiencies and to be able to collect and store massive volumes of data.
However, enterprises cannot unlock the potential of analytics without data scientists and machine learning tools. The knowledge of these tools and technologies sets data scientists apart from most technical professionals. With a huge demand for these skills, engineers in both IT and non-IT job roles have a significant opportunity to thrive in this field, especially considering the urgent need for skilled professionals in the IT sector.
The opportunities in Data Science
Data Science is rapidly emerging as a lucrative field for IT and non-IT professionals and making a career move to this domain can promise huge payoffs. Job opportunities for engineers in the realm of Data Science are plenty and pursuing this field can prove to be a wise move for both graduates and professionals. However, becoming a Data Scientist is not as easy as it may sound. In fact, since there are several different skill-technology combinations to consider, aspirants must carefully choose the skills they want to acquire.
A few things must be considered when making this choice.
First, a new skill must be relevant to either their current field or one they wish to switch to. Second, these skills should ideally be suited to their aptitude or complement their existing knowledge and expertise. R and Python are presently among the most sought-after skills today as their scope of application is potentially enormous, particularly for companies operating in the B2C category. Some of these sectors include banking, finance, insurance, telecom, e-commerce, government, consulting and many more.
As organizations today are required to manage millions of Data points, they require the adequate technological capabilities to apply algorithms at scale, which is exactly what programs like R and Python allow. For instance, consider the example of a bank that wants to strengthen its customer acquisition and retention rates.
With a large repository of customer Data to be analyzed, the Data Scientist uses a programming language like R to perform a regression analysis on the bank’s database, to identify customers with the highest possibility of leaving etc. On the other hand, Python is a programming language preferred by some of the top companies of the world like Google, IBM, Facebook, Spotify, and Netflix, among others, to develop a wide range of applications which work on multiple operating systems.
Upskilling your way to a career in Data Science
Considering the demand and applications of Data Analytics in modern businesses, professionals having extensive knowledge of programming languages like R or Python, along with a database querying language like SQL can apply for a wide range of job opportunities and ensure better career growth. However, a prerequisite for aspirants to capitalize on these prospects is to either reskill themselves with new technical capabilities or upskill to build upon their existing knowledge.
What Data skills one can go for?
A functional knowledge of statistics, understanding the concepts of Data Mining, Data Science with R, Python or SAS, application of Machine Learning, Deep Learning and Artificial Intelligence, Software Programming Languages and Database Management, along with Data Visualisation, are a few skills one can choose.
These skills then help learners build a solid foundation for deep knowledge of a few or all skills, depending upon their interest and the requirements of enterprises. Skilling opportunities are available to both freshers/graduates aspiring to become Data Scientists, as well as experienced professionals looking to explore new high-growth job roles.
Acquiring a certification or advanced degree from recognized institutions or from online upskilling platforms can be the first step towards getting started on this new career path. Certified Analytics Professional, Data Science Associate, or PG in Data Analytics, Big Data and Hadoop by Imarticus Learning and other similar certifications are available to engineers and others. The field of Analytics is set to see an even greater boom over the next few years, demanding new skills and flexibility from professionals. If one intends to enter the field or sustain their position in it over the long term, then upgrading skill-set and adopting a lifelong learning approach becomes essential.