Data Science is a relatively young (some would argue old) field, rapidly growing in importance. The modern world would be very different without it, as it has massive impact on our daily lives. Below, we explain data science and its value to your business in a nutshell.
what is data science
Data science is broadly defined as a discipline relying on scientific methods and on applying these methods for solving business needs. KPIs of Data Science projects are apparent. Improve whatever a company is trying to do and generate more revenue as a result.
Modern Data Science is all about multidisciplinary. Its best represents the intersection between mathematics, computer science, and business domain expertise.
The job of a Data Scientist is bound by definition to stand for a different thing in different companies. In certain use cases, questions to the data can be simply worked with an Excel table and good old regression, while others might require Big Data as a resource and Neural Networks to crack the nut. However, both of these count as Data Science.
Due to the close affiliation with business, the job of a Data Scientist is bound by definition to stand for a different thing in different companies. In certain use cases, questions to the data can be simply worked with an Excel table and good old regression, while others might require Big Data as a resource and Neural Networks to crack the nut. However, both of these count as Data Science.
Nothing illustrates the dynamics of the field as much as the various job titles. All you need is to scroll down a few LinkedIn profiles. You will stumble upon Data analysts, Machine Learning Engineers, Big Data specialists, and many more.
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data science & machine learning
Similarly to the buzzword Data Science, Machine Learning is yet another overused term often introduced to demonstrate tech awareness and to be ‘cool’ in a meaningless way. The core of Machine Learning lies in a fascinating concept. An algorithm that can induct outcomes just from the data itself.
It belongs to the field of Artificial Intelligence and often-used method to solve business problems by Data Scientists. Nowadays, this approach is only at it’s beginning of being applied to different areas of human activity. Subsequently, it is expected that the precision of ML models and their potential will only grow. At present, the method is limited by its need for large data sets (read more about Big Data here) to deliver decent prediction accuracy. Many researchers picture ML’s ability to extrapolate robust predictions from much smaller data sets as the next step in its evolution.
Machine Learning can be described as a complex branch of statistical modelling, combining mathematics and computer science. Remember it as yet another resource in the Data Science tool kit. It is an essential asset for making modern data-driven solutions.