Importance of project management in data science

The Importance of Project Management in Data Science
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Introduction
I am applying for your organization’s scholarship to pursue a Master’s degree in Project Management and Data Science. I have experience in data analysis and visualization. I wish to advance my technical and analytical skills as a data scientist as well as enhance my knowledge of project and data management techniques in an international IT and project environment. I believe these skills will help me in next step of my professional development and makes me a more competitive candidate in the field of IT. I graduated with a bachelor’s degree in Computer Science and Software Engineering, GPA 3.7. After graduation, I worked for ACTED and the UN. At ACTED, I analyzed government’s statistical data and socioeconomic vectors of conflicts which helped to enhance the capacity of local government actors and NGOs. At UN Women, I was a junior researcher, drawing conclusions from data. Project Management plays a very important role in data science, hence, will be very useful in my future career.
Body
Project management involves the management of people and resources involved in a project so that the goal is delivered on time, within budget, while adhering to the project requirements (Feeney & Sult 2011). Data science refers to an interdisciplinary field where scientific methods are used to gain knowledge and insights from data that exists in either structured or unstructured forms (Concolato & Chen, 2017).

Analyzing and processing big data is a complex process that needs to be managed effectively, hence, the need for project management skills.
The processes involved in a data science project in many ways resemble the typical project management phases. A typical data mining process would include steps such as understanding the problem, understanding the available data, preparing data, modeling the data using algorithms, evaluating the effectiveness of the models and deploying the models to a real-world use (Concolato & Chen, 2017). These steps are sequential and can also be fitted into the typical project management steps of initiating, planning, executing, and closing. The processes used in data science are well-known in project management. Hence, project management skills will help me as a data scientist to deliver quality final products in good time.
Conclusion
From above it is clear that Project Management plays a very important role in data science and will be very useful in my future career. With an international Master’s degree, I will be able to develop the skills needed to achieve my career goal in management consultancy, specializing in IT. Your organization has a history of providing equal opportunities to education for students from diverse backgrounds by awarding scholarships. I will be happy if you consider my application.

References
Concolato, C. E., & Chen, L. M. (2017). Data science: A new paradigm in the age of big-data science and analytics. New Mathematics & Natural Computation, 13(2), 119-143.
Feeney, M., & Sult, L. (2011). Project Management in practice: Implementing a process to ensure accountability and success. Journal of Library Administration, 51(7-8), 744-763.