3 Reasons Why Teamwork is an Essential Skill in Data Science
This article will discuss 3 important reasons why teamwork is so crucial in real-world data science projects.
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- Without teamwork, real-world data science problems would be impossible to solve.
- Academic training programs should therefore design capstone projects that would help students to collaborate and build their teamwork skills.
Andrew Carnegie once said: “Teamwork is the ability to work together toward a common vision. The ability to direct individual accomplishments toward organizational objectives. It is the fuel that allows common people to attain uncommon results.”
Teamwork is one of the most important skills required for success in data science practice. This article will discuss 3 important reasons why teamwork is so crucial in real-world data science projects.
1. Lack of Domain Knowledge
A data scientist might not have domain knowledge about the system of interest. For instance, depending on the organization you are working for, you may have to work with a team of engineers (industrial dataset), doctors (healthcare dataset), etc., in order to figure out what predictor features and target feature to use in the model. For example, maybe an industrial system has sensors that generated data in real-time, in this case, as a data scientist, you may not have technical knowledge about the system in question. So you will have to work with engineers and technicians for them to guide you to decide what features are of interest and what are the predictor variables and the target variable. Teamwork is therefore essential to piece together different aspects of the project. From my personal experience working on an industrial data science project, my team had to work with system engineers, electrical engineers, mechanical engineers, field engineers, and technicians over a period of 3 months just to understand how to frame the right problem to be solved with available data. Such a multidisciplinary approach to problem-solving is essential in real-world data science projects.
2. Scope of the Project
In a real-world data science project, the dataset is often very complex. For instance, the dataset could contain thousands of features and maybe hundreds of thousands or even millions of observations. In addition to that, the dataset could also be dependent on both space and time. Hence it could be very challenging to figure out what kind of model to build, and what features to use for building the model. The scope of the project may be too huge that it can’t be tackled by a single person. In this case, the burden of the project can be made lighter by working with a team of other data scientists, data analysts, data engineers, as well as personnel from the industry. By delegating tasks to different team members, the workload can be made lighter and manageable. This would allow the project to be planned, designed, and executed within a reasonable time frame.
3. Business Implication of the Model
How will the final model be deployed? How does the company executives and managers view the project? Are they willing to invest in the idea and implement recommendations for day-to-day decision-making? Will the recommendations improve the efficiency of business operations? Will it lead to improved customer experience? Does it lead to an increase in profit? What are the financial implications of the model in terms of cost-savings? As a data scientist, you will have to work with company leaders and executives to try to convince them that your model or deliverable is worthy and has far-reaching implications that could save the company lots of money. In order to determine the business implications of your model, teamwork will play a great role as you will have to work with engineers and other officials from the business department to determine what cost-savings your model will produce if implemented.
In summary, we’ve discussed 3 important reasons why teamwork is important in real-world data science projects. Without teamwork, real-world data science problems would be impossible to solve. Academic training programs should therefore design capstone projects that have industrial implications. If possible, the academic programs could invite local companies to suggest and recommend capstone projects. Students working on the project will have to be fully engaged in all aspects of the project from problem framing to data analysis, model building, testing, evaluation, and implementation. Collaborations with industry officials throughout the duration of the project have to be prioritized. That way students can have the opportunity to work on a multidisciplinary and diverse team. Such a multidisciplinary approach to problem-solving will enable them to develop essential teamwork, communication, leadership, and business acumen skills that are required for success in the real world.
Benjamin O. Tayo is a Physicist, Data Science Educator, and Writer, as well as the Owner of DataScienceHub. Previously, Benjamin was teaching Engineering and Physics at U. of Central Oklahoma, Grand Canyon U., and Pittsburgh State U.