Data Science Workflow: How to Create and Structure it Simplified 101

on Data Science, Data Science Workflows, Tutorials • April 6th, 2022 • Write for Hevo

When dealing with less mature industries like Data Science, there isn’t always a textbook answer. Therefore, whenever data scientists start a new data science project, they must consider the project’s specifics, their previous experience, and their personal preferences when setting up the data source, Modeling, and Visualization.

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.

In this article, you will gain information about Data Science Workflows. 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. Read along to find out in-depth information about Data Science Workflows.

Table of Contents

What is a Data Science Workflow?

Data Science Workflow
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Workflows describe how people perform tasks to complete a project. A well-defined Data Science Workflow is useful because 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.

It is beneficial for team members to keep track of work and what needs to be done by using a well-defined Data Science workflow. This reminds you of what has been accomplished and what needs to be done.

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What are the steps in a Data Science Workflow?

Because in data science problems, sometimes, we don’t know the end from the beginning – so it’s tough to define a concrete template that should be universally applied when studying Data Science problems. In other words, the roadmap of how you would approach a task will vary based on the issue and the data, so it’s up to the team to define a structure that works. 

Yet, we often see the same workflow when approaching many data science different problems, irrespective of the dataset. So let’s look at this workflow.

However, you should note that the process outlined below is in no way linear. Instead, most Data Science projects are largely iterative, requiring multiple stages to be repeated and revisited. 

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The steps involved in the Data Science Workflow are as follows:

Step 1: Problem Definition

Ensuring that the right problem is being addressed is not as easy as it may seem to define a problem. You need to consider these questions while describing a problem: 

  • 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? 

Before beginning any Data Science project, the most important part is to identify and state the problem statement clearly. This will serve the purpose of your project. 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

It is critical to get the right kind of data for any Data Science project. It is necessary to obtain all the relevant data, format it into a form that can be analyzed, and clean it before starting any analysis.

Acquire Data

When you have a problem to solve or a goal to achieve, you must collect the data that will power the project. A Data Science Workflow begins with the acquisition of data.

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.

While collecting data, it is essential to remember the following points:

1) Data Provenance

To conduct new experiments, data often needs to be re-acquired later, so it is crucial to track provenance, i.e., where the data came from and if it still applies to the current experiment. A data re-acquisition can be beneficial if the source is updated or if alternate hypotheses are being tested. Also, provenance can trace errors back to the original data source.

2) Data Management

When a company creates or downloads data files, it is critical to assign proper names to those files and then organize them into directories to avoid duplication and confusion between versions. The names of all versions of those files should be corresponding when new versions are created to make it possible to track their differences. For example, hundreds or even thousands of data files are created in scientific labs, which must be renamed and arranged before any computational analysis occurs.

3) Data Storage

There is almost no limit to the amount of data accessed on a day, so it must often be stored on remote servers because a hard drive cannot hold it all. Despite the popularity of cloud services, significant amounts of data analysis are still performed on desktop computers with data sets that fit on modern hard drives (i.e., less than a terabyte).

4) Data should be Reformatted & Cleaned

Data that has been formatted by someone else without considering the analysis in mind is not commonly in a convenient format for analysis. Additionally, raw data often contains grammatical errors, missing entries, and irregular forms, so it must be “cleaned” before being analyzed.

5) Data Wrangling

This is cleaning your data, organizing everything into a workspace, and ensuring that your data contains no errors. The data can be reformatted and cleaned either manually or by writing scripts. In some cases, converting integers to floats, for example, may be required to get all the values in the correct format. Afterward, a solution must be found for the null and missing values that characterize sparse matrices. 

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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.

Your goal is to understand the data to enable yourself develop hypotheses that could potentially be tested once you get to the next step in the workflow i.e., modeling the data

Step 4: Data Modeling 

When you’ve explored the data in-depth, you will have a much better sense of the type of challenge you’re facing, and hopefully, you would have generated some hypotheses which you can test out in the following stage. 

Due to the nature of Data Science, you are likely to be required to test a wide range of solutions before deciding how we want to proceed. 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. By 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.

Performing data analysis is essentially a trial-and-error process: a scientist performs tests, graphs the results, repeats the tests, graphs the results, etc. The use of graphs can visually compare and contrast their characteristics since they can be displayed on monitors side-by-side. Both digitally and physically, note-taking is a valuable tool for keeping track of the thought process and experiments.

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. These stakeholders are typically not well-versed in Data Science, so being able to alter their message in the form of appealing visualisations which will aid in the understanding of the stakeholders.

Conclusion 

In this article, you have learned about the Data Science Workflow. Reproducibility is crucial to the success of data science workflows since they are iterative by nature. In addition, data science is a team activity. To determine your team’s workflow and how to structure projects, assess your team members, and come up with a set of practices that are customized to fit the values and goals of the team as well as serve as a good idea to assess existing frameworks and see what could be taken from them.

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