Data is one of the key components of any business. Most companies collect and
Why is Data Driven necessary?
Data Science helps companies not onlyincrease its efficiency, but also bring great income. The situation with a large amount of data has led to the formation of Data Driven - a managerial approach to decision-making, which is based on the use of data, as well as their analysis using specialized tools and methods. At the same time, data is the main source of information and the basis for decision-making. This approach is used in marketing, finance and medicine and is useful for improving the efficiency of business processes and making optimal decisions.
Data scientists are an integral partdata driven approach. They are engaged in the analysis of large amounts of data in order to extract useful information and use it to improve business processes and decision making. This includes various tasks such as collecting data, cleaning and preprocessing it, building models and algorithms for data analysis, as well as visualizing results and communicating insights in a business context.
Medicine, marketing, banks
Machine learning algorithms help doctorsanalyze images obtained using computed tomography or three-dimensional x-rays. Based on the data, they model the effects of drugs, identify in advance ineffective and dangerous combinations of substances based on their molecular composition.
Analysis and prediction of the level of sales for variousgoods depending on the price, season or a certain cyclical demand is a classic task that is solved by all retail chains on an industrial scale. In addition to predicting demand, such organizations need to solve a whole class of logistical problems.
The banking sector is one of the fastestimplementation of machine learning approaches in the organization's processes. Estimation of the maximum loan amount, recognition and segmentation of documents, automatic classification of user requests: in any of these tasks, machine learning helps not only improve the quality of decisions made, but also significantly speed up the process.
Data Science in Aviation
However, there are areas in which the use of machine learning helps solve non-obvious problems - for example, aviation.
In view of the established standards and rules, this area is extremely conservative and demanding on the reliability of the developed systems.
It is known that a significant part of the flight (atprovided there are no extreme weather events), the aircraft operates in automatic mode: the main load on the pilots falls during the takeoff and landing of the vessel. Airbus is developing the ATTOL system, an automatic takeoff and landing system. The company is positioning the product as the first automatic system of its kind, including computer vision techniques that help the system analyze the condition of the runway. The complexity of developing such systems is associated not only with minimizing any possible errors of machine learning algorithms, but also with the difficulties of integrating them into aircraft avionics, training pilots, and the high cost of testing.
Another example of the use of machine learning inin the aviation-related field - automation of pre-flight control for passengers. Delta Airlines introduced a system in 2021 that allowed passengers on domestic flights to go through all pre-flight procedures in a fully automatic mode. It was enough for the passenger to register in the application and take a picture. When visiting the airport, the passenger simply approaches a specially installed camera and the system allows him to board. Automation of such processes reduces the burden on airline staff and saves passengers from queues.
Airline aggregators often facethe task of recommending certain destinations to passengers. By analyzing the user's purchase history, one can assume potential dates and destinations that may be of interest to customers. Depending on these factors, you can not only successfully recommend specific flights, but also form a certain price that the user will be willing to pay. Dynamic pricing is a common task that developers solve in a variety of client services: online stores, taxi services, airline tickets. Such services often involve a whole range of algorithms: recommender systems, time series analysis, regression algorithms.
The need for automation is manifested not onlyin the field of passenger aviation. Cargo aviation is also among the candidates for the use of machine learning methods. In this case, they can help at several stages: the optimization of supply chains helps not only to reduce costs, but also to reduce the amount of fuel consumed, which has a positive effect on the environmental component. The introduction of computer vision methods helps to take a step towards the automation of the entire flight: takeoff and landing systems, flight control and environmental analysis - a set of such algorithms helps to reduce the burden on pilots.
Data Science in Agriculture
Another area of application of machine learning approachestraining - agricultural industry. Cognitive Pilot is actively engaged in equipping combine harvesters of various agricultural enterprises. Among the hardware components of the autopilot, there are two cameras that capture the space in front of the car and transmit information to the neural network that makes the decision to correct the route. This approach allows you to unload the managers of combines, allowing them to focus on the content of the harvesting process and improve the quality of the resulting crop.
In addition to automations on the ground, algorithmsmachine learning is being actively introduced into the processes of space monitoring, which help to assess the state of crop lands on a larger scale. The increasing number of satellites makes it possible to accumulate large amounts of data that can be used to train various mathematical models. Depending on the data collected, algorithms can help analyze soil conditions, detect degenerative processes, crop conditions - these are just a few of the tasks that machine learning can help solve.
An integrated approach in agricultural technology is calledprecision (or precision) farming. The idea of the approach lies in the large-scale integrated support of agricultural processes. In the fields, various sensors are used to record various indicators: humidity, acidity, and so on. Satellite photographs or unmanned aerial vehicles allow you to assess the condition on a larger scale and obtain generalized information. To aggregate this information, Data Science methods are actively used, and machine learning algorithms are also used to obtain recommendations for care and yield forecast.
The field of precision farming is extremely activeunder study: in 2021, a report by the UN Development Program was released, which identified several key areas for the development of such farming at once: monitoring weather and soil conditions, monitoring the dynamics of insect pests and plant diseases, various types of plant irrigation. Among the hardware tools that can be used in these processes, literally everything from smartphones and drones to components of the Internet of things.
Data Science in Chemistry
The introduction of data science methods is also happening inother areas of knowledge. One of these areas is medical chemistry, one of the areas of which is the development of new types of antibiotics. One of the extremely serious problems that humanity will face in the near future is the resistance of bacteria to already developed antibiotics. The speed of creating new drugs with the desired properties is an extremely long, complex and expensive process, in which machine learning methods and neural network modeling are already helping scientists. At the Massachusetts Institute of Technology, the Department of Biological Engineering has developed a platform for the analysis and development of new antibiotics, which is able to test millions of chemical compounds and select potential combinations suitable for the treatment of bacterial inflammation. One of the drugs developed using this platform has shown good results in the fight against several dangerous bacteria that are resistant to other antibiotics.
In addition to the direct result - new drugs -such approaches can “filter out” substances that are known to be dangerous or simply useless, so scientists can focus only on potentially effective drugs. The active introduction of such methods and approaches can significantly improve the quality of pharmaceutical products, and therefore have a positive effect on life expectancy.
Data Science in the Humanities
In addition to scientific and industrial fields, a dynamicdevelopment can be expected in more familiar areas. For example, with the development of models that allow generating images, the approach to the development of game universes in computer games may change significantly. Given a small data set of a certain style, an artist or game developer can generate a large number of potential character or object models for a future computer game. Fans of different games: Red Alert, Fall Out and others regularly share their creativity, creating images in the spirit of their favorite games. In addition to the graphics component, game developers also state the need to use machine learning models to analyze player behavior in a multiplayer game in order to eliminate challenging or toxic behavior.
Modern models can not only helpgenerate fantastic characters: a lot of space opens up for fashion specialists and clothing designers. In creating new ones, you can use various neural networks in different ways: get the necessary thing from the text description, draw a sketch of the thing and specify the materials, color - and get the finished version. Other machine learning algorithms can help with virtual fitting - such applications are already available in the app stores of most smartphones.
Significant progress has been made in the development andapplication of text models. The recently released chat model ChatGPT from OpenAI shows amazing results in the field of text generation. The model can be asked to write an essay on a given topic, implement an algorithm in a specified programming language, or solve a logical problem. The model is, in a certain sense, universal: it “understands the text” and is even able to correct its own results if it is pointed out to erroneous elements in its answers. Users of modern models successfully combine the results of their work: for example, they receive textual results in the form of a description of some world or situation, run the results through graphical models and receive images as output.
Development of data science in recent yearshas radically changed our lives: everyday things that we take for granted are almost always the product of one algorithm or another. Recent years have shown that a sharp leap in development has also demonstrated many problems: text models that can answer questions or generate arbitrary texts based on the beginning of a sentence given to them are often prone to discriminating against different forms, generative graphic models can be used to create fake photographs, etc. However, Data Science as a field will play an important role in the future in solving many complex problems: climate change, environmental protection, ensuring a healthy lifestyle, creating new technologies, innovations.
In modern companies, the process of collecting and analyzingdata is one of the key elements, in this regard, the demand for specialists in this field is only increasing. Many companies are looking not only for highly qualified specialists with specialized education and work experience, but also for novice employees who have completed retraining courses and are ready to continue developing in their chosen field.
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