By Dr. Akhter Mohiuddin Rather, Associate Professor, Machine Learning & Data Sciences, Great Lakes Institute of Management, Gurgaon
In the fast-evolving landscape of artificial intelligence (AI) and machine learning (ML), it’s easy to be drawn toward the allure of large language models (LLMs) and generative AI (GenAI). These tools, while powerful, often give a false impression that mastering AI can be achieved quickly by simply learning how to use them. However, true mastery of data science comes from understanding the foundational principles that drive them, not just the tools themselves.
The Importance of Core Skills in Data Science and AI
Data science is built on a foundation of statistics, mathematics, programming, and algorithms—essential pillars that enable the creation, interpretation, and optimization of AI models. While tools like LLMs and GenAI offer quick solutions, they often obscure the complexities of data processing, feature engineering, and model evaluation. Over-reliance on these tools without understanding their underlying principles can limit one’s ability to innovate or solve complex problems.
Mastering the basics—such as linear algebra, probability, and core ML algorithms—ensures a strong skill set for real-world challenges. Employers prioritize professionals who understand algorithms and model development over those dependent on pre-built tools. By focusing on fundamentals and engaging in hands-on projects, students and professionals can differentiate themselves as innovators in a rapidly evolving field.
True success in data science lies in the ability to build and improve AI systems from the ground up, fostering critical thinking and problem-solving skills. This foundational expertise not only opens doors to advanced applications but also ensures long-term career growth in the dynamic landscape of data science and AI.
Business education should support employers’ talent expectations and students’ career goals by developing the required competencies in MBA graduates. Many studies in the US have found gaps between the competencies desired by the industry and those possessed by MBA graduates. Research has identified several unmet skills, including crucial analytical and technical abilities, which today’s top business schools fail to teach. A study comparing graduates’ employability skills with job advertisements found significant variations between what employers want and the attributes possessed by new graduates. Furthermore, a cross-national study of students in data science disciplines revealed that while employers seek strong analytical, programming, and statistical skills, students often feel they are lacking in these foundational areas. It was observed that students and professionals in business schools tend to rely too much on tools, including AI tools, while neglecting the fundamental principles of data science. This over-reliance hampers their ability to apply critical thinking and problem-solving skills in real-world scenarios. There is a pressing need for business education to emphasize foundational skills in data science alongside practical applications to better prepare graduates for the demands of the industry.
Data science and ML are not fields that reward shortcuts. The ability to work with data effectively, analyze it rigorously, and derive insights comes only with a strong foundation built on first principles. While LLMs and GenAI are valuable tools, true mastery comes from understanding the underlying mechanics of AI, allowing professionals to rise above the crowd and shine as experts in their field.
Coding is an essential part of data science, and true data science enthusiasts must earn their skills through hands-on practice. In the early days, before 2015, when deep learning frameworks like Keras, TensorFlow, and PyTorch didn’t exist, building neural networks required hardcoding in languages like C and C++. Those early pioneers who manually coded neural networks were the ones who deeply understood the core mechanics behind the models. They didn’t rely on pre-built tools; instead, they grasped the intricate math and logic driving the algorithms. Today, with Python dominating the field, coding has become much simpler. Implementing ML and data science models is easier than ever, thanks to user-friendly libraries and frameworks. But this simplicity can sometimes deceive newcomers into thinking mastery can be achieved without real coding skills. Instead of relying on shortcuts from generative AI tools, it’s crucial for students to focus on learning the fundamentals of coding and algorithm design. Mastery comes not from copying but from understanding the building blocks and logic behind the models. Learning the fundamentals—not just the tools—is key to excelling in data science.
