In today’s digital economy, data science has emerged as a critical driver of innovation, strategic decision-making, and competitive advantage across industries. Organisations increasingly rely on data-driven insights to anticipate trends, optimize operations, and create value, making data science one of the most sought-after skill sets globally.

Against this backdrop, Prof. Abhijit Dasgupta, Director of the Bachelor of Data Science programme at SP Jain School of Global Management in an interaction discusses the foundational skills, practical applications, and cross-sector relevance of data science. Excerpts:

  1. How would you define data science, and why has it become such a critical driver of innovation and strategic decision-making in today’s digital economy?

Data science is an interdisciplinary field that combines statistics, computer science, domain expertise, and scientific methods to extract meaningful insights and knowledge from structured and unstructured data. It encompasses the entire data lifecycle—from collection, cleaning, and processing to analysis, visualization, and interpretation—using techniques ranging from traditional statistical analysis to advanced machine learning and artificial intelligence.

Data science has shifted from being a specialized technical function to becoming a core organizational capability that informs strategy across all business functions, creating what many call “the world’s most valuable resource”—not data itself, but the intelligence derived from it.

  1. How is data science being applied across diverse sectors such as business, healthcare, finance, marketing, and public services, and what makes it so universally relevant?

Data science transforms organizations from reactive (reporting what happened) to proactive (anticipating what will happen) to prescriptive (guiding optimal decisions). This transition represents perhaps the most significant operational paradigm shift since the industrial revolution, explaining its cross-sector dominance.

The field’s true power lies not in any single algorithm, but in creating data-informed cultures where decisions at all levels are enriched by evidence, experimentation, and continuous learning from data. And this advantage is shared across any and all domains.

  1. Why are strong foundations in mathematics, statistics, and analytical thinking essential for building long-term capability in data science?
  • Mathematicstells you what you can do with the data.
  • Statisticstells you what you should do and what you can claim from it.
  • Analytical Thinkingtells you why you’re doing it and how to translate results into action.

For long-term career resilience, the goal is to be the architect who designs the solution based on first principles, not just the craftsperson who assembles prefabricated parts. The foundations are what enable that transition and ensure your skills remain valuable amid rapid technological change.

This gets to the very heart of what separates a sustainable, impactful data scientist from someone who merely applies tools. Strong foundations in mathematics, statistics, and analytical thinking are not just “nice-to-haves” for data science; they are the fundamental operating system upon which all tools, algorithms, and code are built.

  1. How relevant is data science for learners and professionals coming from non-technical or hybrid backgrounds like commerce, economics, management, or engineering?

Data science is not just for computer scientists or statisticians. Professionals from commerce, economics, management, and engineering bring critical domain expertise and problem-framing skills that pure technicians often lack. Here’s why data science is highly relevant — and valuable — for those from non-technical or hybrid backgrounds:

Organisations increasingly seek “T-shaped” professionals:

  • Vertical bar: Deep skills in their domain (e.g., supply chain, marketing, finance)
  • Horizontal bar: Breadth in data literacy — able to work with data scientists, understand models, and use insights

Roles like Business Analyst, Product Manager, Operations Research Analyst, Marketing Scientist, or Policy Analyst now require data proficiency as a core skill.

Gartner predicts that by 2025, 70% of new applications will use AI/ML — but most will be created by non-data scientists via automated tools. Your role will be to:

  • Frame problems
  • Ensure ethical and practical application
  • Integrate outputs into business workflows
  1. For graduates and working professionals looking to upskill, what learning pathways, courses, or certifications can help them transition into data science roles effectively?

Recommended Pathway by Background

For Complete Beginners

  1. Preferred – Full-time classroom program or if that is not possible due to financial and family constraints then Online academic program leading to a degree (PG/UG)  / certificate from an academic institution. GaTech, Harvard, MIT, several IITs, SPJain, BITS Pilani et al have excellent programs in Data Science (both contact and online courses)
  2. Google Data Analytics Professional Certificate (Coursera) – Gentle entry point
  3. IBM Data Science Professional Certificate (Coursera) – More technical follow-up
  4. Build portfolio with 2-3 end-to-end projects (Use datasets from Kaggle/UCI)
  5. Specialize based on interests (ML engineering, analytics engineering, business intelligence)

For STEM/Engineering Graduates

  1. Fast-track through fundamentals (focus on gaps in statistics/probability)
  2. Deep Learning Specialization or ML Engineering focus
  3. Contribute to open-source data projects or research internships
  4. Target roles: ML Engineer, Research Scientist, Quantitative Analyst

For Business/Commerce Professionals

  1. Business Analytics Nanodegree from an online education company
  2. SQL + Tableau/Power BI mastery (most immediate workplace impact)
  3. A/B Testing and Experimentation courses
  4. Target roles: Business Analyst, Product Analyst, Analytics Manager

For Working Professionals (Time-constrained)

  1. Part-time bootcamps
  2. Micro-master’s Programs
  3. Weekend workshops and evening certifications
  4. Internal mobility: Seek data projects within current role first
  5. Coursera / MIT Micro Masters pathway
  1. How does a structured data science programme bridge the gap between theory and real-world application through hands-on learning, projects, and industry exposure?

The most preferred transition happens through a structured academic programme, where theory, practicals, capstone projects et al provide a solid foundation to the understanding of data science. While someone can learn the subject bit by bit from online short courses, this might take far greater time than a structured full-fledged academic program, which would get over in 1-2 years. Secondly, there is a danger of over-confidence once someone have learnt a bit of Python and have been exposed to ML libraries like PyTorch. There are far more complications in this discipline than just 2 or 3 topics.



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