The one-word answer is yes! The two disciplines are progressing side by side and changing commerce as we have experienced never before. We, while performing everyday processes with internet-enabled devices generate 2.5 quintillion bytes of data through a collective endeavor. This huge amount of data can neither be stored nor processed. We can only utilize a fraction of the same. Fortunately, that much is enough to propel trade and commerce to a whole new level. And to roll this massive wheel of progress data science and business analytics must go hand in hand. Taking up both courses at the same time is unwise. And taking up one course at a time is the most fruitful approach. However, just taking up only one of them and working in relevant commercial sectors as an intern can suffice for the need for skills that are of value in contemporary commerce. 

Data science is a liberal discipline, and students from any background can join. Provided, they are adequately trained in statistics and mathematics. In addition to that, a data scientist is expected to possess sufficient skills with automation and machine learning tools that are essential for making sense of huge amounts of data. That is humanly impossible to handle or process. However, modern-day data science courses are mostly equipped with automation and programming training. This includes elaborate courses in Python or R. 

A business analyst is expected to be an adept manager with formidable data skills. And statistical prowess to handle gargantuan volumes of data. The primary responsibility of a business analyst is to make sense of huge amounts of data and come up with data-dependent predictions. The data we are talking about consists of both internal and external data. The external data, however, is strictly business and commerce related. And the internal data can lead to a clear understanding of the limitations and capabilities of an organization. Thus a business analyst is not only responsible for charting a safer path towards a secure future. Also, navigate the workforce in an optimized manner so that the chances of failure are minimum. 

The most essential expectation from both roles is hands-on work experience. That too is on the frontlines. Data is key to commercial survival in 2022. And the data concerned roles are highly depended upon. Based on the analyses of a business analyst or data analyst, hangs the fate of entire operations. And employers understand the importance of the same really well. Thus, making a risky hire and involving freshers in their ranks is avoided at all costs. Therefore, just by completing a data science and business analytics course, a student can not be expected to enter the industry as a data professional. To assure a fulfilling career, a student must be transformed into a professional beforehand. And that is possible only through hard work and training in actual industry scenarios. 

A data scientist is trained to utilize all kinds of data, that include business data. Thus the opportunities for a data scientist exceed beyond the sphere of commerce. But for business analysts, their training and experiences in management make them the most perfect candidates in commerce. But due to the versatility, a data scientist is also considered a valued asset in commerce. This section will try to concentrate on opportunities that can be availed by both data scientists and business analysts. 

In marketing, humongous amounts of data are being used. Thankfully, the data we need is ethically obtainable by inexpensive means. All kinds of purchase and investment data are being used to precisely point out the most potential customers. Also by analysis of purchase patterns, the temporal aspects of the campaigns are pinpointed. After the analysis process is complete, a marketing team can point out the ones who require a product and when they are willing to invest in the same. Thus with data professionals having completed a data science or business analytics course at the helm today’s marketing campaigns are precise. And are known for maximum success.

The product is a medium of interaction for a business. Through a product, a business comes into contact with its customers. And the quality and convenience of a product determine the commercial relevance of a business. Thus keeping the product relevant is of the essence. A data professional at the helm of product management usually handles large amounts of end-user feedback data. And by analysis of this data, the upgrade requirements are figured out. In addition to that, a business analyst is concerned with planning an upgrade operation by utilizing huge amounts of internal data. And makes sure that the workforce is not stressed or that the financial status of a business can support long-term operations. 

In the case of business administration, a business analyst is preferred more over a data analyst. However, by working in relevant fields a data scientist can acquire the necessary set of skills that are expected in the sector. An administrator is responsible for the utilization of both internal and external business data and charting a safe passage through the most precarious of times. The external data is used for financial, marketing, and business strategy formation. And the internal data is used for understanding if the existing infrastructure can support the execution of those plans. In addition to that, an administrator is responsible for the comprehensive delivery of analysis results with utmost lucidity. Every involved or interested party must get a clear view of the bigger picture and understand the weightage of their roles in the same. Only then, responsibility and dedicated hard work can be expected from an individual worker. 

Conclusion 

It is recommended for enthusiasts looking for the best data science and business analytics course. Must consider relocating to industrial cities. Places where data education and industry thrive together. As, only by being amidst all the relevant activities, a student can get the necessary exposure to build a relevant skill set and a strong professional network. Essential achievements that can ensure a sustainable and long-lasting career in commerce for both data scientists and business analysts.