As we have all heard, Glassdoor ranked the data scientist as the best job of the year. Curricula are appearing in some of the most prestigious universities around the world trying to meet market demand. These programs focus on technical skills associated with employment, but these skills are only makes good data scientist.
The value of the data specialist for an enterprise is not that they can apply statistical modeling to the data to generate a model. A data specialist must understand the needs of the company and develop analyzes that meet those objectives.
Analytical results can include improved customer engagement, automation leading to cost optimisation, or business process optimisation to save time and labour. However, the real value comes from providing the results that match the real needs of the companies.
The data science is as much an art as science. The data specialist should have in mind a general idea of what a great solution looks like. Mediocre solutions are abundant. Finding the right solution for a just situation requires patience and determination.
A data scientist must continue to push towards the solution that will optimize the value of the company. Without passion for business and passion for the field of study, a data scientist will not be short of finding this optimal solution.
Data science is not a new field, but new discoveries are made every year. This is because the great scientists of the data are always looking for alternatives to solve problems. This includes finding new and optimal ways to acquire and merge data, preprocesses, and engineering capabilities, or to develop models and improve run time using a combination of software and hardware optimisations .
Part of the value in the science of data is coming up with solutions that were not thought and executed before. In digital business, the prime-engine advantage is real and can make or break a business. Many new business models depend on how they can harness data and analysis to produce a new and innovative model so that data specialists can not simply duplicate what has worked before. They must always be looking for the next big thing that will distinguish their offer from others already on the market.
Although the mathematics involved in the analysis is fundamental and proven, using it to solve specific business problems is an art form, as mentioned above. The data specialist must be able to differentiate between large and not so large analyses.
Precision measurements are an excellent mechanism for testing the readiness to produce a model, but the data specialist must be able to feel when a model is ready. They also need this intuition to know how flawed production models are and must be refactored to meet an ever-changing business environment.
While technical skills are paramount to the success of a data scientist, many important features are inherent and can not be taught in a classroom. These characteristics can be acquired, but it takes time and practice and requires an internal desire.
The combination of learned skills and intrinsic characteristics is makes good data scientist apart from a mediocre one.