Data scientists deal with finding solutions for complex problems, by analyzing technology, algorithms, and complex data structures. They deal with troves of raw data that takes 70 to 80 percent of the time to data mining activity and sorting the same into a readable format. This data is used creatively by the data scientists and applied in various fields. Business decisions that are made based on this data bring accurate results. Data is also used to understand the trends and future demands of the product.
How data science is used by different companies
Data scientists work across different companies and on different formats of data. For instance, a data scientist working on Netflix would use data to understand movie patterns. A retail company selling FMGC products would use a data scientist to understand the count of future demand for their products and enhance their production levels. Data is explored and data insights are created by these scientists. The sorting and mining of data gives a panoramic view of how data can be utilized in various departments. There are different models used by data scientists for various projects.
Data science in different models
A common personality trait of data scientists is they are deep thinkers with intense intellectual curiosity. Data science is all about being inquisitive – asking new questions, making new discoveries, and learning new things. Ask data scientists most obsessed with their work what drives them in their job, and they will not say “money”. The real motivator is being able to use their creativity and ingenuity to solve hard problems and constantly indulge in their curiosity. Deriving complex reads from data is beyond just making an observation; it is about uncovering “truth” that lies hidden beneath the surface. Problem solving is not a task, but an intellectually-stimulating journey to a solution. Data scientists are passionate about what they do, and reap great satisfaction in taking on challenge. Source: Datajobs.com
Data scientists work on mathematics, algorithms, technical information, and business strategies to come up with insights from raw data. They need skills in three areas to be able to refine data that can be produced for decision making.