What is data science? Data science is a scientific discipline that searches for truth and uses data to generate knowledge and ideas. Data science is developing rapidly and is already of great importance for every industry and field of science. Nevertheless, it is still at an early stage of development.
Data Science market is growing at a cosmic pace
Back in the very recent past, when business learned to collect data, but did not yet know how to fully benefit from it, one could often hear the opinion that big data would simply bury corporations under its own weight. Now there are fewer and fewer such exclamations, and the reason for this was the rapid development of Data Science.
It would seem that a relatively fresh concept is actually present in the life of almost every person. For example, Data Science is at the heart of targeting Google ads, creating personalized recommendations on YouTube or Netflix. The importance of this approach can be proved by absolute values: the market volume of platforms for Data Science in 2017 was $ 20.2 billion, and its average annual growth rate (CAGR) in the context of 2025 will be at the level of space 39.7%. By the way, the market of tools for big data analytics, in which solutions related to Data Science in one way or another, will sooner or later begin to dominate, although it shows a slightly lower CAGR (12.3%), but by 2027 it will reach $ 105 billion. …
The reasons for this growth lie in the ability of Data Science and Data Scientists to use a wide range of tools to infer working business hypotheses from big data and look for interrelated factors. The dependence of business on this direction has obviously become colossal and will only increase over the years. Determining the needs of the consumer and the ability to offer him the right product in the right way, eliminating routine in offices and defective products in production, as well as a relatively acceptable price tag for the implementation itself.
Artificial Intelligence and the Internet of Things are driving data science
The main reasons for the development of Data Science were breakthroughs in certain technologies, which made it possible to use the potential of this approach more fully than, for example, in the second half of the last decade. Experts call the rapid increase in the number of projects using artificial intelligence as one of the key trends. AI is now used in almost all business segments and industries and is constantly evolving. Developing business along the way: artificial intelligence is able to increase the efficiency of business processes by tens of percent, as well as profits. This is achieved through more reliable and automated customer data management. In addition to AI, data science is also affected by automated machine learning, which is still gaining momentum.
Another growth driver has been that the Internet of Things has finally made its way into real-world applications in recent years. Investments in this area by the end of 2020 should amount to $ 1 trillion, and this is far from the limit. The increase in the number of smart and connected devices generates huge amounts of data that can no longer be simply stacked on a data center shelf – they are too valuable. This is especially true for highly loaded industries, such as industry.
The gradual expansion of the boundaries of big data analytics, which is a satellite of Data Science, also played a role. Companies began to receive more valuable information about consumers, cleanse data and give it to Data Science platforms for post-processing. A special role here is assigned to predictive analytics, thanks to which data Scientists can build images of companies for the short and even medium term. This applies both to global stories, such as writing marketing campaigns, and to local business processes, such as filling warehouses.
Another factor, albeit not so obvious, experts call the development of edge computing. Edge Computing solves the problems of channel bandwidth, preventing them from overloading and reducing data latency. In addition, the possibilities for their storage and pre-processing directly at the collection points are expanding, which even the manufacturers of video cameras have already taken advantage of.
At the same time, the world is moving towards expanding the profession of data scientist itself to related industries. In Data Science, all work is usually built in Python. It is as complex as a language, but specialists who speak it are able to quickly and gracefully solve problems related to the security of enterprises, which seriously raises the weight of Data Science as a scientific field. The demand for such employees will grow exponentially over time.
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