6 Steps of Data Science Project Life Cycle
In order to build a successful business model, its very important to first understand the business problem that the client is facing.
2. DATA COLLECTION
Data required for the project is collected from various sources, such as databases like PostgresSQL, Oracle, or MongoDB or web scraping, such as Beautiful Soup.
Data preparation is the most time-consuming, yet arguably the most essential step in the project life cycle. It include steps like merging data sets, EDA, treating inaccurate data, etc. A model is as accurate as input data.
A data science model (or algorithm) takes the organized data as input and gives a preferred output. Types of model suitable selection depends on whether the business problem at hand.
Model evaluation technique depends on the model type. The model is evaluated and assessed on unseen data before deployment.
The last step in the data science project life cycle is model deployment. A model will only contribute to solving a actual world problem, only if it is properly deployed and is active.