Impulses and Insights on how to successfully manage Data Science Projects
Published in · 3 min read · Apr 26, 2021
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It takes several factors and parts in order to manage data science projects. This article will provide you with the five key elements: purpose, people, processes, platforms and programmability [1], and how you can benefit from these in your projects.
P 1: Purpose
Just like in the classic approach of project management, a goal or purpose should always be formulated. Possible examples can be:
- Better business insights
- Fraud prevention/detection
- Prediction
- Maximization problems, etc.
It is essential for a project within the field of Big Data or Data Science to have a specific purpose or goal. You should never aimlessly work on a project, just because everyone is doing it, since it will not be useful for you or your company.
P 2: People
Various types of people with different skillsets play an important role within a data science project. In order to work successfully with data, developers, testers, data scientists and domain experts are essential.
Furthermore, stakeholders/project sponsors and project manager/product owner are involved in data projects. In this relation, the former group of people have to be informed who are informed about the progress of the project, whereas the latter have the task of mediating between stakeholders and the development team. More information on how to specifically set up a team can be read here [2].
P 3: Processes
There are two main types of processes within data science projects: organizational vs. technical processes. The following table features questionings for both process approaches:
You have to take two different types of processes into consideration. On the one hand organizational processes and topics like:
Managing data science projects involves a comprehensive understanding of various crucial elements. Let's dive into the concepts outlined in the article you provided:
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Purpose: Defining a clear goal or purpose is fundamental. This entails establishing objectives such as improving business insights, fraud detection, predictive analytics, or addressing optimization problems. Without a specific aim, projects risk being directionless and unproductive.
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People: A diverse set of individuals contributes to the success of data science projects. This includes developers, testers, data scientists, domain experts, stakeholders, project sponsors, project managers, and product owners. Each role brings a unique skill set and perspective, and effective communication among these stakeholders is vital for project progress.
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Processes: Two primary process categories are involved in data science projects: organizational and technical. Organizational processes focus on managerial aspects like project planning, resource allocation, and stakeholder management. Technical processes encompass methodologies, algorithms, and data handling techniques. Balancing and optimizing both types of processes is crucial for project efficiency.
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Platforms: Utilizing suitable platforms and tools is essential for effective data science project management. These platforms may include data processing frameworks, machine learning libraries, cloud services, or specific software tailored for data analysis and modeling.
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Programmability: This refers to the ability to code, automate tasks, and develop algorithms. Strong programming skills, especially in languages like Python or R, are integral for implementing and refining data science solutions.
Each element—purpose, people, processes, platforms, and programmability—interacts intricately in successful data science project management. Achieving synergy among these aspects is pivotal for delivering impactful results and ensuring project success.
