Want to impress recruiters and land your next data science role? Mastering how to run a data science project can set you apart. Check out the key takeaways below and read the full article from Dzidas Martinaitis via the link at the bottom.
Define the problem
A well-defined problem is essential. Questions like “What impact does this email campaign have on site clicks?” or “Does this checkout change boost sales?” give clear direction. On the other hand, vague goals like “increase efficiency” rarely lead to actionable insights.
Identify and engage stakeholders
Business leaders don’t usually hand over well-defined problems. Often, you’ll have to find the team that needs a solution and start working from there. Building from the bottom up, especially in larger companies, tends to go more smoothly.
Scoping the project
Ensure that the project will have a higher rate of return than the effort put in. In addition, check to see if pre-existing solutions are in place that would be less glamorous, but nonetheless provide cost savings.
Setting metrics and success
Before jumping into the work, set clear metrics. Whether it’s model accuracy or a baseline improvement, setting these goals with stakeholders will keep everyone aligned when the project is underway.
Execution
Execution itself is usually the easy part if the steps above are handled. Keep it flexible; some stages might need to be handled one at a time, others all at once. Regular check-ins help make sure progress is smooth and things stay on track.
Remember these five tips when preparing for your data science interviews. Demonstrating your ability to structure a project effectively and communicate your approach will impress recruiters and show your readiness to contribute meaningfully. If you need more guidance, CAPD is here to help refine your strategies and give you an edge.
