Workflow of a Data Science Project

Author(s): Raman Kumar Jha

Data Science

Data science projects are a kind of projects where output is often a set of actionable insights, the insights which may cause you to do things differently.

To understand the workflow of a data science project, we could use an example of optimizing a sales funnel. If there is an e-commerce website that sells coffee mugs. So, for a user to buy those coffee mugs needs to go to the website and take a look at different coffee mugs at the website. After that, they need to go to the product page then add it to the shopping cart, and at the end check out the product.

Samples of Coffee Mugs

So if we want to optimize the sales funnel to make sure as many users as possible will go through the above process then there comes the role of data science. The key steps for a data science project are as follows:

Collect Data:
This is the first step for any data science project to collect data. Collecting data gives us the appropriate information for any particular aspect of our project.
As we are talking about a coffee mug website, so we should have a dataset where we can see which user visited which webpage and from which country the user is?
Analyze Data:
Analyzing data plays a crucial role in a data science project. It tells us about a lot of factors which are affecting the performance of our sales funnel.
Here, we can say as an example that a lot of overseas customers are scared off by the international shipping costs and that’s why they just go to checkout but they do not actually checkout. There could also arise a case that the data science team will observe that more people are shopping around the holidays or fewer people are shopping. There might be a situation that more the advertisement of the website more will be the website visit. So for all these insights, the data science team needs to iterate as many times as possible.
Suggest Hypothesis/Actions:
This is the last step for the data science project. Here the team will suggest some small no of hypotheses about what could be going well and what could be going poorly. They also provide a small no of actions that need to be done for the better performance of the company.
In a coffee mug website, they could suggest incorporating some changes in shipping charges and also change in the time of sale for different timezones of the world.

As the new suggested changes will be deployed on the website then it is also required to reanalyze new data periodically. This will help the team to maintain and grow the projects. The accuracy and efficiency can also be increased by following the steps properly.

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Published via Towards AI