Understanding industry expectations from data science courses and professionals in India

Today, the certified pass outs from the leading data science courses in India have big dreams when they start working with leading companies in data science specialization. These professionals now prefer to start their career with startup companies that offer smaller ecosystems, to begin with, but then progress aggressively with largely profitable compensation options to choose from in the future. In this article, we will highlight the growing expectations from the data science industry and how top data science courses in India could cope with the challenges thrown open by the hiring markets in India.

Is data science still the best market to invest in?

Despite the rapid progress of the data science industry in some areas of business, the pace of adoption within the industry has been largely abysmally low. This is due to the lack of high quality talent that is involved in doing research and innovation work in data science and other related domains. By 2025, if this is not corrected, India could lose up to 1 billion USD every year to its closest competing economies like China. 

Let’s understand the scope of data science applications in India.

In the modern era of B2B technologies, only 1 percent of the global players have been able to justify to their investments in data science tools and technologies, with a majority of the company owners feeling the heat to continue with their digital transformation journeys using data science solutions. Indian CEOs and investors of Indian origin are one of the most forthcoming communities who are working tirelessly in the US to get the attention of global users for their data science projects. 

If this is done in India, the results would be faster and smoother because of the solid foundation in IT, Electronics, and Computer applications that India is so famous for. But, there is a huge gap in the way people transition from their graduation courses in Engineering, Mathematics, and Business Analytics subjects without taking a telescopic judgment about their future in data science. All that is changing, but it will still take some time to fully understand the expectations from the industry.

For example, let’s take the healthcare industry – as one of the major consumers of data science solutions.

Did you know that blood collection unit in various clinics now store data and match them with personal information to understand how different races and ethnic groups react to a pandemic situation? Or, gender based hormone tests could be used to identify the future of human evolution without taking into account the role of environmental factors? Or, how do you estimate the probability of life threatening diseases like cancer or diabetes in small children? Data science is the key to solving all problems related to healthcare issues today. But, then, is talent standing up to the occasion.

Business leaders find it difficult to ascertain this situation gripping the healthcare industry despite the rise of data science. In the recent months, we have seen how big healthcare companies are engaging in a fight for market dominance in vaccinations and personalized healthcare solutions. They are striving for better innovations but falling short due to the urgent necessity for competing to gain superiority in hiring the best talent. 

Unfortunately, the trends have changed in the data science world where beginners, as well as expert professionals in India, are no longer just looking to grab an interview opportunity with the traditionally large IT and Software making companies. They are demanding much more. This has forced organizations to change their hiring tactic to get a more qualified skilled workforce for their healthcare data science projects.

Now, this kind of situation is also seen in other dominant spaces where data science is so widely used, such as IT and security management, manufacturing control systems, marketing and sales organizations, media and entertainment, e-commerce, mobility, and the recent industry to adopt data science, financial services, and banking solutions. Everywhere, the expectation is that people will build and control systems for their business processes, train machine learning algorithms and then pass on the supervision to the AI models that they created. 

But, there is a catch here— AI trainers understand that if they train AI too much in their own trade, they would lose their jobs to their machine counterparts and this is a major hurdle to the way business in AI and machine learning with data science is shaping up. Business owners have to take into account that they are not going to 100% replace the entirety of their human workforce with machines or AI think tanks. That would only cause chaos.

This is what the industry, therefore expects from the leading data science courses in India and the prodigies they create in their classrooms and labs.

Does the new crop of data science professionals understand Responsible AI?

Responsible AI or ethical AI is a big buzzword in the data science domain. It’s clearly a goal for many organizations that are working in AI and machine learning to hire only those data scientists and analysts who understand the dimensions of responsible AI. Today, any team that knows how to handle the demands of R-AI, can safely announce to the world that they belong to an enlightened group of AI workforce that can differentiate any organization and industry with their advanced skills. This is exactly what the industry is calling for— tapping talent from the responsible AI world.

Do you understand the various data governance frameworks?

No doubt, there is an immense potential in the modern day data science applications. However, if we are to succeed with the current league of solutions, we must train with the best tools that explain and train analysts on data governance principles and frameworks. As organizations look to scale up from big data to quality data, it is the responsibility of the data scientists to establish a guiding rail to describe the role of governance at all stages of data operations management, even if it means having people work with machine learning models. 

Different stages of data governance where machine learning applications are required are:

• Benchmarking Planning
• Formulation of data governance
• Monitoring
• Enforcement
• Knowledge and Change management
• Rewards and punishments

As we continue to see the evolution of the data science concepts, we will realize how important it is to stay relevant to the trends. Taking a guess from outside the industry without actually working in a top business intelligence group could make you fall flat on your face.

If you are to succeed with your aspirations in the future, you must rise with advanced analytics, self service and embedded business analysis, and AI based roles. To ensure all data governance policies are aptly applied, and no bias is overlooked, it is important that data science trainers should explain the importance of having a good data governance and data quality from the very first session in the top data science courses in India. 

As a student looking to pursue data science courses in India, this is a great time to fulfill career aspirations and also take the country forward. This would help meet the growing expectations from the industry that uses data science tools extensively for its own projects.