Good Data Science Starts With Asking the Right Questions!

Good Data Science Starts With Asking the Right Questions!

It's easy to get excited about machine learning models, but before choosing an algorithm, we need to understand the problem we're trying to solve.

In my experience, a strong data science workflow begins with a few fundamentals:

Understand the data: Check missing values, outliers, duplicates, and data quality before building a model.

Explore patterns: Use exploratory data analysis (EDA) to identify trends, relationships, and unexpected insights.

Build a simple baseline: Start with a straightforward model before moving to complex algorithms.

Choose the right metrics: For demand forecasting, MAE and RMSE can help measure prediction errors. The best metric depends on the business problem.

Monitor performance: Real-world data changes over time, so models need regular evaluation and updates.

A model is only useful when its predictions help someone make a better decision. Accuracy matters, but business impact matters too.

As I continue exploring demand forecasting and NLP, I'm learning that asking better questions is just as important as writing better code.

Data scientists, what's your first step when you receive a new dataset?

#DataScience #MachineLearning #EDA #DemandForecasting #NLP #DataAnalytics