With the rise of Generative AI, many professionals wonder if learning the old school foundations of statistics, classical machine learning, and data science is still relevant. After all, tools today can generate insights, code, and even models with just a few prompts. It is tempting to skip the basics and focus only on leveraging Gen AI platforms. But the reality is, foundational knowledge still holds significant value, especially depending on who you are and what you do.
For Data Scientists and Analysts
If you are building models, validating results, or making sense of patterns in data, a strong foundation is non-negotiable. Understanding probability, hypothesis testing, regression, and classification gives you the ability to look beyond the numbers produced by a black box. For example, in financial audits, anomaly detection using simple statistical thresholds or sampling still outperforms Gen AI in terms of cost and defensibility. When an auditor has to explain findings to regulators, being able to show transparent, classical methods builds credibility. For data scientists, these skills also help decide when a leaner traditional model is enough and when it makes sense to deploy a more resource-heavy Gen AI solution.
For Business Analysts
Business analysts often work in fast-paced environments where time-to-insight is critical. Here, statistics and machine learning fundamentals offer the ability to slice through data and make sense of it quickly. A business analyst who understands these basics can cross-check the outputs from Gen AI, spot inconsistencies, and use lightweight models for day-to-day reporting. This mix of foundations and Gen AI-driven productivity ensures that the analyst does not rely blindly on whatever the system generates.
For End Users and Decision Makers
For leaders, domain experts, or casual users of data platforms, Gen AI offers a clear edge. It saves time by automating repetitive tasks like summarization, quick forecasts, or drafting reports. These users may not need to worry about the math behind the servers running Gen AI. What matters more is the ability to interpret results in the context of their business. For them, Gen AI is a productivity booster, while the burden of validating outputs remains with the analysts and data scientists.
Finding the Balance
Gen AI is a powerful tool, but it is not a silver bullet. The future belongs to those who can balance both worlds. For data professionals, the foundation knowledge of statistics and machine learning ensures accuracy, fairness, and trust in every model deployed. For end users, Gen AI is about efficiency, speed, and saving time without diving into technical complexities. Knowing when a simple linear regression will suffice and when a large-scale model is justified is what separates good analysts from great ones.
In short, the value of foundational knowledge depends on the persona. For data scientists and analysts, it is an essential skill set. For business users, Gen AI is about convenience and acceleration. Together, these perspectives ensure organizations make the most of both traditional data science and modern AI.
The article thought was inspired from a post by Dhaval Patel Ref: Link1
LinkedIn Article: https://www.linkedin.com/pulse/era-gen-ai-should-we-still-learn-statistics-ml-praveen-nair-9fpsc