Best practices for data science projects
10 Best Practices For Data Science Projects
The 10 best practices for data science projects that assist you in resolving real-world problems
The field of data science has earned the reputation of being the next big thing in technology and business. In recent years, the number of businesses using data science applications has only increased.
We’ll go over some of these best practices for data science projects in this article, which businesses can use to boost their data science efforts’ success rates. There are many practices for data science projects. But first, let’s learn more about the idea of data science projects. We have enlisted 10 best practices for data science.
First Choice: To get the support of the business, begin with a quick-win use case.
You must focus on use cases that share three essential characteristics:
- A business leader who is ready to take responsibility for their success. The champion is essential for establishing the use case’s business significance and gaining executive and general business support.
- Clearly defined KPIs to evaluate the impact on the business of the results. These are necessary to show business stakeholders the measurable impact of your project before and after.
- Accessible, available, and clean information. If your data isn’t of high quality or readily available, you run the risk of turning your quick-win into a data cleansing exercise, which is not something you want to do if you want to maintain the interest of the business.
Second Best Practice: Establish a strong Data Science organization and team.
One of the most common mistakes we’ve seen is failing to involve IT early in the project’s ownership. This is necessary to avoid getting stuck in Proof-of-Concept (POC) limbo. Projects are typically assigned to squads led by experienced data scientists or engineers, with a mix of data scientists, data engineers, visualization experts, architects, and data science storytellers among the squad members.
Third Best Practice: Choose the right tools and metrics for the job.
When it comes to metrics, it’s important to choose the right ones to connect data science results to business objectives. Predictive algorithms, for instance, are frequently evaluated using the Root-Mean-Square-Error (RMSE) metric; however, depending on the underlying business objective, the Logarithmic-Root-Mean-Square-Error metric may yield superior results. Metrics, on the other hand, are typically business KPIs like revenue or cost for optimization algorithms.
Fourth Best Practice: Establish an early POC dashboard for business stakeholders.
Gaining business support necessitates the early creation of a POC dashboard for business stakeholders. Participate in a Design Thinking workshop with business stakeholders to begin your project to accomplish this. During this meeting, come up with concepts and think about what a dashboard that is the result of the project would mean to them.
Fifth Best Practice: Spread the word widely and frequently.
Through regular reviews of the project’s progress, you can maintain business buy-in. In these reviews, let a stakeholder in your company lead the presentation. Instead of presenting the results in code, make use of your POC dashboard to present them in business language.
Sixth Best Practice: Use an Agile strategy.
An Agile Data Science approach should be used to guarantee consistent progress. This means that your project should be broken up into sprints of two to three weeks, with sprint reviews at the end of each cycle to show examples of the results achieved and Agile task planning. To contain the project’s scope, manage risks, and reduce uncertainty, invite all stakeholders to the sprint reviews and sprint planning.
Seventh Best Practice: Make provision for adaptable infrastructure.
When scaling up is necessary, the necessary infrastructure is not readily available, which is one of the primary reasons why Data Science POCs do not progress into the real world. The POC is then put on hold until infrastructure is acquired, which typically takes a long time or until the POC is forgotten.
Eighth Best Practice: During the POC phase, ask operational questions.
Find answers to operational questions like how often models will need to be tuned, how much data will be ingested (e.g., streaming vs. a scheduled job), how much data will be produced, and how much hardware will be needed during the POC.
Ninth Best Practice: Prepare a strategy to put your POC into action.
From day one, begin planning how to put your POC into production, and include a production plan in your final POC sprint review. You may be working with a subset of the data during the POC period; to implement your POC in the real world, you must also consider other data requirements, such as governance, volume, and the role of data stewards.
Tenth best practice: Optimized actions should replace insights and predictions.
Predictive analytics are required for understanding what might happen in the future; traditional business intelligence tools provide insights into the state of the business. In turn, predictive analytics can help you understand what might happen in the future, but it can’t tell you what to do about it; prescriptive analytics is needed for that.
The post 10 Best Practices for Data Science Projects appeared first on Analytics Insight.
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