A Data Scientist is a person who specializes in extracting the essence from large amounts of data using machine learning. The results can include visualizations, user interfaces, text, and numbers, and can be used both directly by decision-makers and as part of a larger or smaller data system.
There are specialized education programs in machine learning (ML), but a Data Scientist often has a different background in the natural sciences and extensive programming experience, transitioning to working with machine learning as a starting point. The work is closely tied to preparing proprietary datasets and analyzing them before machine learning and visualization can be used to draw conclusions.
It is also often better to use experience-based calculations combined with machine learning—especially in analyses related to physical, chemical, and biological processes, such as in a hydropower plant or a salmon farm. In these cases, there are so many known factors and relationships that it is not very practical to rely on machine learning for everything. An experienced Data Scientist knows when a problem can be better solved with methods other than machine learning alone.
To derive real value from machine learning, good data systems for collection and storage are required, and effective analyses and visualizations require a comprehensive understanding of business logic. The use of real-time algorithms as part of a larger data system also requires an understanding of all programming layers. A skilled Data Scientist, therefore, has the ability to delve into both the business aspect of the problems and create solutions that fit into a holistic data system.
Cegal and Data Scientist
Cegal Data Scientists assist clients in creating tailored solutions for machine learning and analysis. Our consultants in this field specialize in power grids and also have experience with video and text analysis.
An example is a project for one of our energy clients, where we delivered a solution for visualizing which ground cables should be replaced or maintained first, based on historical data and machine learning. With many individuals having extensive experience in the organization, there are varied opinions on how to prioritize replacement and maintenance. The solution provides the client with a mathematical reference point for these opinions, contributing to the decision-making process.
At Cegal, we are committed to meaningful work, and one of the projects we are engaged in, in addition to our client work, is a volunteer project with KRIPOS where we work to combat child abuse online. In this project, our Data Scientists analyze log files to increase understanding of file exchange traffic and create visualizations to assist the police in their ongoing work.