After I was learning data science and machine learning at university, the curriculum was geared heavily towards algorithms and machine learning techniques. I still remember those days cracking the mathematics, not exactly fun, but nonetheless a rewarding process that had given me a solid foundation.
Once I graduated and commenced working as a knowledge scientist, I soon realized the challenge: In real life, problems rarely present themselves as nicely formulated and readily addressable by machine learning techniques. It’s the info scientist’s job to first define, scope, and convert the real-life problem right into a machine-learning problem, before even talking in regards to the algorithms. This is an important step as completely different approaches could also be adopted depending on how the issue is formulated, what’s the specified final result, what data is accessible, the timeline, the budget, the computing infrastructure, and lots of other aspects. In a word, it shouldn’t be a simple arithmetic problem anymore.
This gap in my data science training made me feel disoriented and pressured to start with. Luckily, I had my mentor and project colleagues, who helped me quite a bit in picking up the essentials and learning to ask the correct questions. Step-by-step, I became more confident in managing data science projects.
Reflecting alone experience, I actually wish I could have the prospect to learn those soft skills in data science to higher prepare for my skilled life. Now I actually have undergone the struggles, but is there anything I could do for the newly graduated data scientists?
A famous book for preparing interviews in management consulting is “Case in Point”. This book provides quite a few practice case studies that cover a wide selection of topics and industries. By observing and understanding how those case studies are solved, the candidates can learn quite quite a bit in practical problem-solving processes and be ready for real-life challenges.
Inspired by this case-study format, a thought occurred to me: Can we leverage the recent large language models (LLM) to generate relevant, diverse…