Data Science Life Cycle: 101 on the Key Stages

You may have already heard the expression “data is king”, and in the world of business, it really is. The insights that are held within your company’s digital information can help enhance your operational processes and boost overall performance. Thus, it’s no surprise that data science adoption is growing across industries.

3. Analytical Algorithm Development

Finally, the next step of the data science life cycle is all about building the data model. At this point, the digital information is taken as the input so that the preferred output can be prepared.

As you can probably imagine, there’s a variety of data science use cases across industries. So, depending on the kind of problem you have, the most suitable type of model needs to be selected to acquire the needed results.

Most of the goals you’re trying to achieve can be accomplished through statistical analysis, classification, regression, clustering, or anomaly detection. Each of these methods has universally known state-of-the-art approaches. In this regard, experienced data scientists will help you navigate the options and settle with the right one that suits your unique use case.

Remember, it’s important for the model to deliver accurate results and have generalizability. Otherwise, it won’t be suitable to run on new data. For this reason, testing the model is an absolute must.

4. Model Testing

To ensure that the final data science product actually benefits your business, it’s important to test the developed model prior to deployment. That’s the only way you can assess quality and result accuracy before implementing it into your IT infrastructure.

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Model evaluation can be done through the hold-out method or cross-validation. During the former, the dataset has to be divided into two subsets, with one being used for training and the other for testing.

On the other hand, during cross-validation, generalizability is assessed by splitting the dataset into ‘k’ groups and using one for testing and the others for training. Then, this process is repeated so that each of the groups has been used for testing.

Overall, cross-validation is preferred for model testing over the hold-out method. This is because it accounts for more variance between splits and is more dynamic. It’s good to keep this in mind; however, experienced data scientists will help guide you through this step of the data science methodology.

5. Solution Deployment

Following extensive model testing, it’s time to implement your data science solution in the preferred channel and format. At times, you may want to deploy it to a set of users or into a test environment; once again, it all depends on what your project aims to do. Some require the tool to be introduced in a limited capacity as an MVP app until it is proven to be successful, before rolling out full-scale implementation.

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This is the stage of the data science life cycle that stakeholders have been looking forward to the most. Yet, it’s important not to rush the process.

As we said, deployment will look different for every software initiative. For some, it may be as simple as getting the model output onto a Power BI dashboard. For others, it will entail deploying it to the cloud or embedding it into a mobile app for hundreds, if not thousands, of users.

By the end of this, your data science model can finally work with a real-time inflow of data and generate the outputs you were looking for.

6. Support

Finally, the phases of the data science life cycle conclude with performance monitoring and support. At this stage, results and feedback from users are collected to evaluate if there are any immediate tweaks that are necessary.

Often, the development team will continuously support the implemented solution and refine it to keep up good performance standards. After all, you wouldn’t want to spend all this time and money on a new piece of software only to let it deteriorate with time.

Additionally, it is at this point that you might want to assess whether the final solution has solved the initially set-out challenges and what the effect of its implementation is on the bottom line.

Start Your Next Data Science Project

Data science can deliver a multitude of advantages to organizations across industries. So, it shouldn’t be overlooked as a worthy IT initiative. Now that you’re familiar with the six main phases of the data science life cycle you will be better prepared to start your next project.

At Velvetech, we pride ourselves on the successful data science services we have delivered over the years and would be happy to share our expertise with you. So, if you’re looking to get some help on your next IT undertaking — get in touch with us.