Teamwork is Essential in Data Science
By: Benjamin Obi Tayo Ph.D.
Tags: Real World, Machine Learning, Capstone Project, Teamwork, Data Science

Teamwork is Essential in Data Science

Without teamwork, real-world data science problems would be impossible to solve. Sharing personal experiences on the importance of teamwork in an industrial project

“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.” Andrew Carnegie

Teamwork is one of the essential skills required for data science practice. This article will discuss 3 important reasons why teamwork is so crucial in real-world data science projects.

In academic training programs, we often work with very simple datasets and the problem to be solved is well defined. For instance, a homework problem could provide you with a clean dataset and it might ask you to use a certain set of features as your predictor features, and one feature as your target. The instructions could go even a step further to tell you what kind of model to build (linear regression or classification, in the case of supervised learning). While a capstone project provides an opportunity to apply data science knowledge and skills to a broader problem, most often, these capstone projects are assigned as an individual project. An individual capstone project doesn’t provide the opportunity to develop teamwork skills. Moreover, the assigned project may not have any business implications.

Teamwork is an essential skill that has to be emphasized in academic data science training programs. One way to implement this would be to expose students to real-world data science capstone projects, where they work with industry officials in all aspects of the project from problem framing, to data analysis, model building, testing, evaluation, and implementation. Working on a real-world industrial project with business implications would enable students to develop not only teamwork skills, but also good communication, leadership, and business acumen skills.

Let’s now discuss 3 important reasons why teamwork is a vital skill in real-world data science projects.

a) 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.

b) 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 the right time frame.

c) Business implication of the model

How will the final model be deployed? How does the company executives 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? Does 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 the 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 has to prioritize. 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.

Additional Data Science/Machine Learning Resources

Data Science Curriculum

Essential Maths Skills for Machine Learning

3 Best Data Science MOOC Specializations

5 Best Degrees for Getting into Data Science

5 reasons why you should begin your data science journey in 2020

Theoretical Foundations of Data Science — Should I Care or Simply Focus on Hands-on Skills?

Machine Learning Project Planning

How to Organize Your Data Science Project

Productivity Tools for Large-scale Data Science Projects

A Data Science Portfolio is More Valuable than a Resume

Feature Selection and Dimensionality Reduction Using Covariance Matrix Plot

Data Science 101 — A Short Course on Medium Platform with R and Python Code Included

For questions and inquiries, please email me: benjaminobi@gmail.com