Steps of Data Science Workflow and How to Apply Them

Unlocking essential steps of data science workflow and how to implement them in your projects

This tutorial will teach you about several common data science workflows. Additionally, you will develop a comprehension of the Data Science Workflow's structure and the factors that must be taken into account when using it. Discover the steps of data science workflow and how to use them in this article.

To address various types of data science problems, the data science industry currently provides a variety of data science process frameworks. A comprehensive data science workflow cannot be created to address every business issue.

What is a Data Science Workflow?

A workflow is a planned order of tasks that someone follows to finish a project. Real-world data science problems are complicated in nature, requiring careful consideration of many different scenarios from problem definition to deployment and value realization in order to be properly solved.

The stages necessary to properly complete a data science project are described by a well-defined data science workflow. It aids the data science team in monitoring progress, preventing misunderstandings, comprehending the causes of delays, and being aware of the anticipated schedule for putting data science projects into practice.

Step 1: Data Science Problem Framework Phase

Defining a data science problem may seem straightforward, but it's vital to align it with organizational and business needs. Precision in problem definition is crucial, as it shapes your project's direction and team's actions. Individuals bridging data and business expertise play a pivotal role, ensuring alignment between teams and upholding key principles.

Key steps for effective problem definition:

Involve individuals with both business and data acumen.

Allow sufficient time for rigorous problem definition.

Aim for a clearly defined problem-solving approach benefiting the business.

Step 2: Data Acquisition Phase

You cannot perform data science without high-quality data. One of the most crucial aspects of your data science workflow is acquiring the correct quality of data from various sources, and you will spend 60% to 70% of your time doing so. Prior to analysis, all pertinent data must be gathered, put into an analysis-ready format, and cleaned.

There are several sources:

CSV files on your local machine, Google Spreadsheets, or Excel

Data obtained from SQL servers

Online content delivered via an API

Step 3: Data Exploration

when the data scientists must take their time to become familiar with it. It is crucial to come up with hypotheses during this stage as they examine the data for patterns and anomalies. Check to see if the problem statement suggests a supervised or unsupervised method first. 

You should make an effort to comprehend the data at this phase so that you can create hypotheses that can be tested once you reach data modeling, the following step in the workflow.

Step 4: Data Modeling

In this crucial phase of data science, you delve into building the model, whether it's classification, regression, or another type. Given the iterative nature of data science, exploring multiple approaches is essential. Here are the three key steps:

Learning and Generalization: Develop a machine learning algorithm using training data, a fundamental starting point.

Fitting: Assess if the model can generalize to new, unseen data akin to the training set, gauging its adaptability.

Validation: Evaluate the trained model against distinct data, different from the initial training set, to ensure its robustness and reliability.

Step 5: Reflection or Inference Phase

Data scientists oscillate between the analysis and reflection phases. Analysis involves coding, while reflection involves contemplating and sharing analytical outcomes. Teams or individuals may experiment with scripts, settings, and outputs, scrutinizing results meticulously.

Data analysis is inherently iterative. Scientists run tests, visualize findings, rerun tests, and compare results. Side-by-side graphs facilitate visual comparisons, aiding in the assessment of distinct qualities.

Step 6: Communicating & Visualizing Results

In a Data Scientist's journey, early focus on Machine Learning evolves into a realization: soft skills matter. Effective communication becomes paramount as findings need frequent sharing.

Data Scientists convey insights, conclusions, and narratives to diverse stakeholders. Given stakeholders' limited data science knowledge, compelling visualizations are invaluable for conveying complex information and enhancing understanding.