What Every Data Scientist Should Know about Project Management

Data science is a burgeoning field with subfields differentiated by their unique approaches to the collection and uses of information and data. Across fields and industries, data science professionals are required to work on projects that produce a specific deliverable for an institution or individual. Data scientists do not only benefit from learning more data science tools but also from learning the skills required to manage a data science project. Every data scientist should familiarize themselves with the different approaches to project management, as well as how they can be used to expand their career in data science or open up new opportunities in other fields and industries.

What is Project Management?

Project management is the theory and science of collaborating with team members to accomplish a shared goal. This goal is usually the completion of a specific project and/or the creation of a project deliverable or presentation which can be shared with stakeholders. Much of project management is focused on the planning of various stages in the project, which takes into account the costs of project completion as well as assigning roles and responsibilities to various team members. Data scientists who specialize in project management have the skills to lead a team of analysts and stakeholders in the completion of a data science project.

Project Management and the Data Science Life Cycle

In many ways, understanding the phases of project management is very similar to understanding the data science life cycle, as the two can be combined in order to successfully take a data science project from start to finish. The five phases of project management are most commonly represented by the waterfall methodology which focuses on a linear process from the beginning to the end of a project. The following five phases work through these steps to completion.

1. Initiation and Identifying the Problem - In the data science life cycle, the process of data collection and hypothesizing is the first step to working on a data science project. In project management, this first stage focuses on understanding the problem that needs to be addressed as well as identifying who and what will be involved in the process of solving that problem. This stage also outlines any potential future plans for the project or dataset, such as storage and database management.
2. Project Planning and Data Exploration - Once the problem is understood by the team members and stakeholders, the second stage in the process is viewed as the planning stage. It is during this second stage in both the data science life cycle and project management that team members begin to explore the possibilities of the project or dataset. This could include things like mapping out the dataset and making plans for analysis.
3. Project Execution and Data Organization - After gaining a greater understanding of the data and the plan for the project, it is then up to the team members to execute the project. Within data science, this execution requires the careful organization of information and data, so that the dataset can be analyzed effectively.
4. Project Monitoring and Data Modeling - The fourth phase of project management is less active, and focuses on overseeing the processes that were set into motion during earlier stages of the process. It is also during this stage in the data science life cycle, that data science professionals focus on presenting their own process models which reflect how and why a dataset was analyzed in a particular way. In a sense, this data modeling acts as a demonstration of the team members’ adherence to the project plan.
5. Project Closing and Deliverables - In the final stage the project comes to a close, which usually results in the presentation of a project deliverable. This is also the stage in which all of the initial plans for the project, which are established at the beginning of the project management process, should be carried out or completed.

Besides traditional methods, data science project management can be inclusive of agile frameworks which prioritize teamwork and collaboration. While agile project management is commonly applied within software engineering and the development of mobile applications, it can also be applied when working on a data science team. By taking an iterative approach to the development of a project, agile project management is a methodology that encourages the repetition of the stages that are represented in the data science life cycle and the phases of project management. This process allows for more flexibility and the reality that many data science projects unfold over time, and do not always end the moment that the project deliverable is completed and/or presented to the stakeholders.

Project Management Roles for Data Scientists

Once data science students and professionals have learned more about project management, they can access new opportunities both within and outside of the data science industry. Especially as data scientists and analysts are sought after within multiple fields, data scientists with a background in project management are well-positioned to take on roles as a Consultant, Project Lead, or another type of senior or supervisory role. Through their knowledge of the fundamentals of project management, data scientists can expand their skills in leadership and resource management, which is essential when planning a long-term project and managing team members that come from different backgrounds. Moving up the ranks, Senior Data Scientists and Data Science Project Managers are able to apply new and innovative methods of not only managing individual projects and teams but potentially standardizing the process of working on a project within a company or institution.

Want to Expand your Project Management Skills?

For data scientists who want to learn more about building up their skills in beginning and completing projects, Noble Desktop offers courses in both data science and project management which contribute to these skills. Noble Desktop’s Data Science Classes include several bootcamps and certificate programs that focus on taking a data science project from start to finish. The Data Science Certificate also includes the fundamentals of building a career in data science which is predicated on building a portfolio of data science projects. These data science classes not only expand skills in programming and machine learning, but also teach students and professionals how to present project deliverables.

Noble Desktop’s data science classes and certificate programs can also be supported by taking one of the project management courses. While many of these courses focus on successfully completing a Project Management Certification Exam, they also offer instruction in the basics of working on a team and organizing tasks. For example, the Project Management Bootcamp includes an introduction to the five phases of project management, as well as how to create a project proposal and deliverables. Data scientists who build up a strong resume of analytic and project management skills are an asset to any team or assignment.