A growing number of data science projects has led to an increase in the demand for data science managers. It is natural to think that any project manager can do the job or that a good senior data scientist will make an excellent data science manager. But this is not necessarily true.
Data science management has become an essential element for companies that want to gain a competitive advantage. The role of data science management is to put the data analytics process into a strategic context so that companies can harness the power of their data while working on their data science project.
Data science management emphasizes aligning projects with business objectives and making teams accountable for results. It means ensuring that each team is in place, whether under the same office or as a distributed team. It also ensures that the team members are provided with appropriate roles and people contributing towards the project’s success.
Remember, data science management is about transforming data into valuable customer insights and ensuring that these insights are acted upon appropriately by all stakeholders across the organization. Therefore, Data science without effective management is like playing chess without knowing how to move your pieces.
This guide will dive into some key focus areas for data science projects. You will understand the differences between different stages and how to tackle them effectively depending on your end goal with the project. We’ll also go over some strategies for optimizing data science projects and areas that may be considered challenging due to their complexity.
1. Engage stakeholders
2. Manage people
3. Know data science
4. Define the process
5. Don’t assume great data scientists make great managers
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