Project Management in Data Science
What is Data Science?Data science is an exciting field that impacts nearly every industry and many aspects of everyday life. In general, data science is a broad field that leverages scientific principles to develop actionable insights and strategies from data. It is often depicted as sitting at the intersection of computer science/hacking skills, math/stats, and domain knowledge. However, in practice, the results are often not what you expect. why not? The main reason for this is that organizations struggle to apply effective project management practices to data science. To overcome these challenges, data science project managers can drive project success by applying the right project approach that addresses the unique aspects of data science.
What does a Data Science Project Manager Do?A data science project manager is responsible for executing one or more advanced analytics and AI/ML projects. The job description typically reads like any of her other IT project management roles, but with a focus on specific applications of data science. Shared responsibility includes:
- Develop and communicate project roadmaps for data science projects
- Coordinate and monitor day-to-day tasks and workflows of the project team
- Manage stakeholder requests and expectations; provide updates to project sponsors
- Scope and define tasks that fulfill the project vision; manage and document scope using a project management ticketing system such as Jira, Atlassian, or Rally
- Identify and gather data sets necessary for projects
- Proactively identify opportunities and provide recommendations to improve operational efficiencies and implement scalable solutions
- Remove impediments that hinder the team’s productivity
- Ensure the project team and the resulting project output comply with regulatory, ethical, and legal needs
- Manage a cycle of deliverables that meet timeline and resource constraints
TIPS for this type of projects:
1. Embrace the ChallengeManaging a data science project can seem overwhelming, especially at the beginning of the project. You may be responsible for providing an unknown solution to a vaguely defined problem. You may not even know if the solution is possible! But accept this challenge with a smile. Your attitude and confidence are often the difference between project failure and success.
2. Use a Project Management Basic Framework
- resource management
- schedule management
- scope management
- risk management
- stakeholder management
- budget management
- vendor management
- effective communication
3. Be AgileData science projects typically face obscure problems and unknown solutions. Therefore, we need to define a clear problem space and steer the project to find solutions that solve those problems. This is where agility shines. So just follow these agile principles:
- Use iteration. In data science, these iterations are often the exploration and validation of hypotheses. Each iteration should provide insight to help prioritize future iterations.
- Keep iterations as small as possible while keeping them meaningful. The Data Science Minimal Viable Product concept is a useful construct that helps achieve this.
- Get feedback on every iteration. Everything revolves around learning, especially at the beginning of a project. So get your feedback as soon as possible. The powerful data elements of the project enable you to learn from your data. But also get feedback from stakeholders.
4. How to manage your boards? How to prioritize and collaborate w/ your team?A good project manager fosters collaboration not only within the project team, but also across a wide range of stakeholder and partner teams. There are many different frameworks you can use. Understand these different frameworks and when to use which framework in which situation.
- Kanban – A lightweight framework focused on minimizing work in progress and maximizing throughput.
- Scrum – A product-oriented framework focused on incremental batches of work delivered in fixed-length blocks of work.
- Data Driven Scrum – A variant of Scrum for data science projects.
- de Graaf, R. (2019). Managing Your Data Science Projects: Learn Salesmanship, Presentation, and Maintenance of Completed Models. Germany: Apress.
- Dubovikov, K. (2019). Managing Data Science: Effective Strategies to Manage Data Science Projects and Build a Sustainable Team. United Kingdom: Packt Publishing.
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