How to Create and Structure Data Science Workflow

In this article,You will also gain a holistic understanding of how to structure a Data Science Workflow and what should be kept in mind while following the different steps in a Data Science Workflow.

Although there is no one-size-fits-all Data Science Workflow, following some best practices is essential, such as setting up automated documentation processes and conducting a post-mortem after a project is complete to identify any potential areas for improvement.

What is a Data Science Workflow?

• Workflows describe how people perform tasks to complete a project.
• It provides a simple way to remind all data science team members of the work that needs to be done to complete a data science project.

What are the steps in a Data Science Workflow?

• Step 1 – Problem Definition
• Step 2 – Data Preparation Phase
• Step 3- Data Exploration
• Step 4 – Data Modeling
• Step 5 – Reflection Phase
• Step 6 – Communicating & Visualising Results

Step 1: Problem Definition

• What problem do you want to solve? 
• What are the current issues you are facing? 
• How are your customers experiencing challenges with the product/service you offer? 
• Which insights would be most interesting to you? 
• This step serves as a guide for the rest of your project and informs how the other steps will be carried out.

Step 2: Data Preparation Phase

Data can be acquired from several sources, such as:

• Using CSV files on your local machine;
• Data retrieved from SQL servers;
• Data extracted from public websites and online repositories;
• Online content that is streamed over an API;
• An automatic process automatically generated by a physical apparatus, for example, scientific lab apparatus linked to computers;
• Data from software logs, for example, from a web server.

It can become a messy process when collecting data, especially if the data doesn’t come from an organized source. Collecting a dataset will require working with multiple sources and applying various tools and methods.

Step 3: Data Exploration

• When the Data Scientists have access to the collected data, time must be spent becoming familiar with the data.
• In this phase, it is crucial to formulate hypotheses while searching for patterns and anomalies in the data. You should determine the problem you are solving, i.e., is it a supervised or unsupervised task? Are you dealing with classifications or regressions? Do we want to infer or predict something? 
  • Supervised Learning involves building a model based on examples of input-output pairs to learn how an input maps to an output.
  • Unsupervised Learning identifies patterns in unlabeled data by building a model based on the data.
  • A Classification is Supervised Learning that deals with a modeling problem that results in a discrete label as an output.
  • Regression describes a model whose output is continuous and is a form of Supervised Learning. 

Step 4: Data Modeling 

This involves three stages: 

• Building a Machine Learning algorithm involves learning and generalizing it based on training data. 
• The fitting process involves examining whether the machine learning model could generalize to never-before-seen examples similar to the data it was trained on. 
• Validation involves testing a trained model against a set of data that is different from the training data. 

Step 5: Reflection Phase

• Data scientists commonly alternate analysis and reflection phases: the analysis phase is centered around programming, whereas the reflection phase involves thinking about and communicating analysis outcomes. B
• Examining a set of output files, data scientists, or a team of data scientists, can compare output variants and explore options by adjusting script code and parameters.

Step 6: Communicating & Visualising Results

• Early in their careers, many Data Scientists spend a lot of time obsessing over Machine Learning Algorithms and keeping up with the latest advances in the field.
• However, as time passes, these same individuals realize their attention should be diverted to soft skills. 
• Data Scientists must be able to communicate their results because they will be doing this a lot.
• Data Scientists would have to share findings, results, and stories with various stakeholders during this phase.

Conclusion 

In this article, we highlight the importance of reproducibility in data science workflows, as they are always evolving and refining. Being able to consistently reproduce results is crucial for building on previous work and making progress. Data science is also very much a team effort, so it’s important to think about how to structure projects, assess the strengths of team members, and develop practices that align with the team’s values and goals. By doing this, teams can create an environment that supports collaboration and growth. Additionally, this approach can be a great way to evaluate current practices and figure out what can be borrowed or improved from existing frameworks.

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