Data analytics has moved from the margins to the centre of business operations. Hospitals, banks, logistics companies, retailers, and public institutions all rely on data.

The United States remains one of the most sought-after destinations for Indian students, many of whom spend years preparing for higher education there. They believe that studying in the US offers access to high-quality education, hands-on learning, and the skills required in a rapidly evolving job market. The country continues to attract Indian students with its academic reputation, global recognition of degrees, strong job prospects, quality of life, robust education system, and healthcare benefits.

In recent years, there has been a clear shift in the US toward valuing applied data skills over traditional academic routes. To understand the skills and talent American employers prioritise today, NDTV spoke with PK Agarwal, Dean of the University of California, Santa Cruz Silicon Valley Extension.

Here is the full interview:

We're seeing a decisive shift in the US where employers value applied data skills over traditional academic routes. What is driving this trend, and why are Indian students increasingly choosing Data Analytics as their fastest pathway into the US job market? 

Agarwal:

The shift we are witnessing is not away from education but toward usefulness. US employers still value formal degrees, but they are now far more focused on whether a graduate can apply what they have learned in real-world settings. Increasingly, they look for candidates who can contribute from day one, show adaptability, and continue learning as technology evolves. Across industries, decisions are now driven by data, and the ability to interpret information has become a core workplace skill rather than a technical specialisation.

Data analytics has therefore moved from the margins to the centre of business operations. Hospitals, banks, logistics companies, retailers, and public institutions all rely on data. Employers want professionals who can interpret this information, derive meaning from it, and use it to guide decisions-not simply manage tools or systems.

Indian students have responded pragmatically to this shift. They are choosing analytics not because it is fashionable, but because it is versatile and defensible. It allows them to demonstrate skills early, move across industries, and deliver value in a competitive job market. Analytics is no longer a shortcut. It has become the foundation.

Short-term, skills-first analytics programmes have become popular among international students. In your experience, which elements-practical training, industry exposure, or project portfolios-make the biggest difference in employability outcomes?

Agarwal:

Employability is not a function of course duration. It is a result of preparation. Short-term programmes succeed only when they stop teaching tools in isolation and begin teaching judgment through application.

The students who succeed are those who have worked with real datasets, confronted ambiguity, and defended their conclusions. They are not trained to merely execute instructions; they are trained to interpret situations. When learning mirrors professional environments, confidence develops naturally.

Equally important is instruction that comes from industry. Students learn differently when teachers bring real cases into the classroom instead of theoretical examples. A portfolio built from serious projects tells an employer far more about a candidate than any certificate ever could.

The truth is uncomfortable but necessary: credentials do not create professionals. Experience does.

Many students today are opting for intensive analytics certificates instead of full-length master's degrees due to rising costs and visa uncertainty. How should Indian students evaluate the return on investment when choosing a short-term analytics route in the US?

Agarwal:

Education today must be evaluated with clarity, not emotion. Families rightly worry about cost, time, and uncertainty. But return on investment is not measured by fees alone-it is measured by readiness. A degree offers depth and theoretical grounding. A skills-led programme offers speed and practical alignment. Neither path is inherently superior. The real mistake is choosing either without understanding the purpose it serves.

Students must examine outcomes, not branding. Does the programme accelerate employability? Does it expose them to real work conditions? Does it develop judgment and decision-making, not just familiarity with technology? In a volatile global environment, the most responsible choice is not the longest one-it is the one that makes you professionally useful sooner and adaptable longer.

There's growing evidence of international students stepping directly into mid-level data roles soon after completing these programmes. What enables such accelerated career transitions, and what does a realistic timeline look like for an Indian student entering the US analytics workforce?

Agarwal:

Titles have not become easier to earn. Preparation has become more serious. Students today are not bypassing experience; they are acquiring it earlier. When education is immersive, project-driven, and closely aligned with industry expectations, professional maturity develops faster. The result is not artificial acceleration-it is earned readiness.

A focused learner with relevant training can become job-ready within a year. Career progression then depends not on credentials but on performance. Responsibility follows competence, not age. What appears to be rapid mobility is actually the compression of learning cycles, not a dilution of standards.

What guidance would you offer Indian students preparing for a data-driven career in the US? What should they prioritise-skills, tools, portfolios, or industry readiness-to stand out in a highly competitive market?

Agarwal:

I would begin by saying this: your first job matters far less than the habits you build. Students must move beyond collecting credentials and start building evidence of competence. Real work should replace rote preparation as early as possible. Tools matter, but understanding matters more. Analytics is valuable only when it informs judgment.

AI will be part of every profession. Do not treat it as a threat or a novelty. Learn to use it responsibly, critically, and purposefully. The future will belong to those who can work alongside intelligent systems without surrendering their own thinking.

Above all, focus not on entering the workforce quickly but on growing within it steadily. Careers are not won at the starting line. They are built by learning continuously as the field changes.
That change has already begun.