Recommended Data Science Workflow Templates

In today’s data-driven world, Data Science has become a vital component for many businesses and organizations to make informed decisions.

Data science involves collecting, analyzing, and interpreting large amounts of data to extract insights and inform business decisions. However, data science projects can be complex and time-consuming and require a structured and systematic approach to ensure their success.

By the end of this blog, you will have a good understanding of the benefits of following a structured Data Science Workflow Template and how it can help you conduct your data science projects more effectively.

What is a Data Science Workflow Template?

Well, it provides a standardized process or framework that outlines the steps involved in a data science project, from start to finish. It helps data science practitioners to follow a structured and organized approach, from defining the problem to implementing the solution.

By following a Data Science Workflow Template, data science practitioners can ensure that they have a clear roadmap to follow, making it easier to communicate their findings to stakeholders and improve the chances of success in achieving the project’s goals and objectives.

There are several templates available, however, we will walk through the two widely used Data Science Workflow Templates in this blog. We will explore the phases and steps involved in each template, and how they can be applied to a data science project.

CRISP-DM (Cross-Industry Standard Process for Data Mining) Model is a popular Data Science Workflow Template that provides a structured and standardized approach to conducting a data mining or data science project. It is composed of six phases:

• Business Understanding: In this phase, the first step is understanding the problem or opportunity the project aims to address. This involves identifying the business objectives, defining the scope of the project, and formulating hypotheses about the problem.
• Data Understanding: Once the business objectives have been defined, the next step is to understand the data that will be used in the project. This involves collecting and exploring the data, identifying data quality issues, and assessing the data’s relevance to the business problem.
• Data Preparation: In this phase, the data is prepared for modeling. This involves cleaning the data, transforming it into a suitable format, and selecting the features that will be used in the analysis.
• Modeling: Once the data has been prepared, the next step is to build a predictive model that can address the business problem. This involves selecting an appropriate algorithm, training the model on the data, and validating the model’s performance.
• Evaluation: In this phase, the model’s performance is evaluated to ensure that it meets the business objectives. This involves testing the model on a validation dataset, assessing its accuracy and generalization performance, and refining the model as necessary.
• Deployment: Once the model has been evaluated and validated, it is deployed in a production environment. This involves integrating the model into the business processes, monitoring its performance over time, and ensuring that it continues to meet the business objectives.

OSEMN is a Data Science Workflow Template that provides a structured and systematic approach to conducting a data science project. OSEMN stands for Obtain, Scrub, Explore, Model, and iNterpret.

• Obtain: In this phase, the first step is to obtain the data required for the project. This involves identifying relevant data sources, obtaining the data, and importing it into a suitable data storage format, such as a database or data warehouse.
• Scrub: Once the data has been obtained, it needs to be cleaned and pre-processed to ensure that it is consistent, complete, and free of errors. This involves removing missing or duplicate data, standardizing data formats, and converting data types as required.
• Explore: In this phase, the data is explored to gain insights and identify patterns that can inform the modeling phase. This involves using statistical methods, data visualization techniques, and exploratory data analysis to better understand the data.
• Model: Once the data has been explored and insights have been gained, the next step is to build a predictive model. This involves selecting an appropriate algorithm, training the model on the data, and optimizing the model’s parameters to ensure that it produces accurate predictions.
• iNterpret: In the final phase of the workflow, the model’s predictions are interpreted and communicated to stakeholders. This involves analyzing the results of the model, visualizing the predictions, and communicating the findings to non-technical stakeholders clearly and concisely.

Following the Data Science Workflow Templates can help ensure that data science projects are conducted systematically and efficiently, and can increase the chances of success in achieving the project goals and objectives.
It provides a clear roadmap for data science practitioners to follow and helps to ensure that the insights gained from data are effectively communicated to stakeholders.

Meet the writer:

Atul, data Science enthusiast, currently pursuing Btech CSE from India, is also managing the Data science Club of his college where he regularly organizes webinars for club members to increase awareness amongst all. Atul loves watching Anime, playing video games and blogging!