Data Science – Surge in Employment Opportunities

We are all witnessing how the COVID-19 infected 2020 has turned the world up-side down. Securing a job or retaining one during the pandemic and restricted economic activity is proving to be daunting to say the least. People in job roles that can be done remotely from home have managed reasonably well. Many others, especially in the retail and manufacturing industries have been the hardest hit. Furloughs, layoffs or rather performance management have become quite common. Some have as yet escaped the wrath with just pay cut. We are into the ninth month but still there is no clarity as to when life will get back to normal.

The tech sector especially cloud services and data analysis and data science technologies seems to have sidestepped the severe impact of COVID, which has otherwise crippled other direct consumer facing sectors. Machine learning and Artificial Intelligence (AI) are going to be a backbone for a business analysis and marketing tools. However, it is critical to comprehend data science and how it is valuable for businesses of the information age. To understand we can take the example of social media networks. The way Twitter, Facebook and similar legacy platforms have leveraged the power of data, such as user demographics and interests, to bring endless solutions for business owners and individuals. Not only the social networking sites, data science has also enabled numerous startups and enterprises worldwide to bring distinctive offerings making improvements in the business as well as people’s lives.

What is data Science?

There are many ways to define the data Science term. However in my learning experience it is about telling a clear and crisp story to the machine in its language to produce the desired result. One can define the data science in endless ways. The data science domain employs mathematics, statistics, computer science, and information science to analyses and understand data. The final results and information are then converted into ideas, solutions, and offerings by the business decision makers.

What is the use of data science?

It is really amazing for a website handler or blogger to see the traffic on their site. It is quite easy to understand the available structured data like that of Google Analytics. Google analytics helps us ascertain the type of users visiting to our platform. But this is not the same for other businesses. Google Analytics cannot help them much. Because there is a lot of unstructured data available with organizations. Analyzing this unstructured data can help the organizations personalize their offerings. And this is where data science comes in so handy. It is used to analyze and draw valuable insights out of semi-structured and unstructured data.

Tools and Technique of Data Science

In data Science various advanced analytics tools and frameworks like Power BI, Apache Hadoop, Spark, Tableau, and programming languages such as Python are used to load the data and analyze it.

The Business Field of Data Science

There is no business which can afford to not informationalize its company data for bringing in more efficiencies. Data churning is the indispensable model of business. Some of the use cases where data science is playing a vital role are highlighted below.

Decision-Making: Data science has enabled businesses and industries in India and across the globe to analyze market trends, study users’ metrics, predict risks, and ultimately make careful decisions. There are a number of case studies such as Flipkart vs. Walmart by Data Flair backing the importance of data science in terms of minimizing losses. Data science has facilitated rapid growth in industries and has minimized their losses.

Understanding Demand: With the ever-evolving competitive business landscape, it has become critical to understand the users and their requirements precisely. With data science, it is possible to create and utilize time-saving automated models to go into users’ purchase history, age, income level, and related demographics. This helps to assess the gaps and come up with ideas and offering.

Predictive Analysis: Weather forecast is a relevant example of predictive analysis. It is required by space research organizations, media portals, and the aviation industry. The data pulled from radars and satellites is analyzed and used to provide predictions related to weather changes and natural calamities.

Fraud Detection: Industries such as telecommunications and social media, which are attracting millions of users and subscribers daily really need to moderate the activities of the users. Frauds such as illegal access, theft, impersonation, cloning, and other similar activities can be monitored and prevented with data science.

Product Development: Doing business in the digital era needs accumulating data and analysis to bypass competition and identify user interests. Businesses in India as well as globally are deploying the data tools not only to ease up the product development process but also to bring the unique offerings that people never know they needed. Airbnb & OYO, for instance, have augmented the entire landscape of the hospitality industry on a global level. Scope of data science in India is undeniably one of the cornerstones of the IT industry today. Enterprises today leverage the power of data to hire, train, and improvise functions within organizations.

Career in Data Science

At the same time, the rapidly increasing demand for data scientists and experts can’t be ignored. IBM has predicted that the demand for data scientists will soar 28% by 2020 (DataFlair). It is the very need of the hour for organizations to hire employees armed with data science to create relevancy with the market requirements and sustain the competitive business environment of the present and the future. All this makes data science a hot career choice. Let’s take a look at the career options for data scientists.

Data Analyst: As a data analyst working for an organization, you are responsible for analyzing large amounts of complex data, identify patterns, and derive conclusions carried forward to make prudent business decisions

Business Intelligence (BI) Developer: As a BI developer, you contribute towards organizational growth by developing strategies. You need to deploy various BI tools and build custom models to help the leaders make better

Data Architect: Data architects ensure maximum performance built by data scientists and developers by testing and demonstrating the results

Applications Architect: Applications architects track the behavior of applications used within a business and how they interact with each other and with the end users

Infrastructure Architect: Infrastructure architects ensure the optimal performance of systems to contribute towards the development of new technologies and system requirements. Cloud infrastructure architect is a good example of this trending role

Enterprise Architect: An enterprise architect works closely with the stakeholders of a company including the directors, VPs, founders, and other top-level managers to build enterprise level IT assets. One also needs to possess managerial skills to be eligible for this role

Machine Learning Scientist: Machine learning scientists understand the binary language behind the applications and models. They perform batch processing of accumulated data to make it readable for data scientists. As a machine learning scientist, you are also responsible for researching new data approaches and algorithms, create data funnels, and deliver solutions

Education Qualifications and Skills Required. Being technical and analytical to the core, the data science domain requires expertise in various disciplines including, mathematics, statistics, IT, and computer science. Some common educational qualifications required to become a data scientist would be, Bachelor’s degree in IT, computer science, mathematics, physics or any related field Master’s degree in mathematics, computer science. A certification in data science and data analytics is always demanded by most of the organizations across various sectors such as healthcare, aviation, automobile, FMCG, and telecommunication

More importantly, a passion for coding and an understanding of logic can very often be a good substitute for the above mentioned degrees.