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Data Science Lifecycle
Think of the data science lifecycle as the blueprint for building a house. Just as you wouldn’t start construction without a solid foundation, a clear plan, and the right materials, you can’t dive into a data science project without following a structured process. Imagine trying to build a house without a blueprint. You might place walls in the wrong spots, use materials that aren’t suitable, or overlook the need for wiring and plumbing. Similarly, skipping steps in the data science lifecycle — like failing to validate your model or not thoroughly exploring your data — can lead to disastrous results. In the world of data science, this could mean unreliable models, poor business decisions, or wasted resources.
This post will take you through the journey of a data science project. Whether you’re working with small data or large enterprise solutions, each stage in this lifecycle is critical to ensuring you create models that deliver value. Let’s dive in!
- Engage with a Client
Every project starts with understanding the client’s needs. This is where we establish a relationship, gather information on their business objectives, and understand the context in which they need a solution. Our primary goal here is not just to get the data, but to grasp the problem space and set clear expectations.
