Data science is where problems are solved by applying sophisticated algorithms on huge data sets to extract business-relevant insights. These results either involve advanced statistical techniques like artificial neural networks, support vector machines, and more, or the handling of huge data sets.
Usually, data science projects demand three important skills – business skills, machine learning/statistics, and coding skills. There are very few chances for you to find someone with experience and expertise in all these three fields; they are basically the unicorns of data science.
However, you can find someone with knowledge of one or more skills at the same time. This is the context where you press the importance of identifying key skills in a data science team and enabling members to collaborate and work together. There are two skills that people usually overlook – product/project management skills, and data visualization skills.
Let’s have a look at each of these profiles believed to be very important to data science projects.
The Ultimate Professors (aka the Algorithms Team)
These are the people responsible for creating and implementing the algorithms in statistical computing language. To be precise, these people are basically Ph.D. or master’s degree holders with experience and expertise in creating/handling data models, and advanced machine/statistical learning techniques.
The key skills of professors may include R, algorithms, Python, and machine learning. The algorithms team has the ability to generate insights and bend data to their will in ways you couldn’t have imagined.
The Super Nerds (aka the Big Data Team)
These are the superhumans who have the ability to handle humongous data without batting an eyelid; finding a needle in a haystack is a child’s play for them. The nerds can not only create databases, or build castles in the cloud, they also possess skills like cloud computing platforms, Big Data, and ETL.
Technically, the big data team is said to be the backbone of data science projects; they are the key to making deployable and scalable solutions. It is also said that about 80% of the time in any analytics project is exhausted on gathering, cleansing, and molding the data.
The Supreme Suits (aka the Domain Experts)
The first two skill sets guarantee top-of-the-line working data models, however, you also need a domain expert/business executive to put this to use. These experts have enough industry experience under their collar alongside presentation and communication skills. The domain experts are the people who usually hold an MBA degree or experience equal to one.
The Dazzling Designers (aka the Visualization and design Team)
Another important group of people in data science projects that you should remember is the designers. This team is essential when it comes to presenting sophisticated results and analysis to people who have no knowledge of the same. The outputs are said to be a natural part of the workday for an end-user. And so, the user interactions and personas become crucial
You may not be building the next multinational tech giant, but a reasonable and intuitive interface is essential. Especially, if you plan on building a guided analytics project.
The Sensational Shepherds (aka the Product Managers)
Product managers are those who manage and bring together a diverse group of people as a team; they are important in extracting work from the team. Managers in data science projects are required to have proper experience and expertise in program management, data science, team management, and client interfacing.
Experience in managing teams is considered a bonus. They are also required to understand data models alongside end-users while guiding the results of the cross-functional team.
Organizations frequently form teams consisting of excerpts who hold only a subset of the above-provided skills. The charisma of gathering and building data, building and optimizing models, and generating reports, is not just fascinating, but also very addictive.
It is also considered one of the most-often repeated mistakes in the world of data science. To avoid such mistakes, you need to be aware of the ‘why’ alongside concentrating on the ‘how’. An equally balanced team is one of the basic necessities on your journey towards solving sophisticated, complex, and challenging problems in data science.
