Data Science Project Lifecycle

We have covered overview of data science and what are the required skill sets for modern data scientists. Now we’ll look at the data science project lifecycle, this is the lifecycle that any data science project should try to maintain.

The first thing we’d do is collect data, so we’d acquire data from relevant data sources. Then we’d ask, “Do I have enough information to construct some kind of analytical plan?” If you do, you will be taken to the cleaning stage. You must sanitise the data. After cleaning the data, you should ask yourself, “Do I have enough data to start developing a model?” And if you do, you must begin studying the data and identifying patterns and trends using statistical models such as linear regression.

Do I have a decent concept of the type of model I want to try, and if so, should we start generating the real model? As a result, we must build and train predictive models. If you have outliers in your data and do not clean it, the model’s findings will suffer as a result.

Is your model robust enough after you’ve completed it? Have we made enough mistakes? And if the model is sufficiently resilient, we go where we interpret and really produce the model. So we utilise the model’s outputs, which are fed into the data, to create some predictions. So that is how a data science project’s lifespan is carried out.