It is the science of big data analysis methods. The data science service aims to extract useful information for the enterprise business and the service sector. Since there are many data on the Internet, and it becomes more and more every day, this is called Big Data. The science doesn't imply the expected result after setting the algorithm to the machine, but the installation of fundamental principles, and the machine learns itself. It is machine learning, and AZU Solution uses it. To improve the performance of machine learning models by providing additional training examples, generative AI can be used.

The applied data science process is based on the methods of mathematical and statistical analysis, as well as knowledge of probability theory. Applied data science consulting services have two tasks: understanding what is happening with the data now and what it promises in the future. Theoretical approaches are implemented by AZU Solution in practice using programming tools (languages, frameworks, and libraries) and services for extracting, processing, storing, and visualizing data.

Whoever owns the information owns the world. Proper analysis and visualization of big Data give an increase in margins and an advantage over competitors who have yet to turn to experts with data science specialization. Transaction analysis, loyalty program, and customer behavior forecasting—this is what AZU Solution engineers work with, taking into account the benefits of data science for business.

No one will give an exact answer because the cost depends on the project's complexity. But by general principles, structures with a limited budget - startups and government agencies - often use data science engineering. In addition, data science tools involve saving resources, and the number of data scientists is constantly increasing along with the competition. So, the AZU Solution answer to the question is no more than money.

While data science and analytics share some similarities, such as data analysis techniques, the main distinction lies in data science's broader scope and objectives. Data analytics is focused on understanding historical data, while the application of data science encompasses a broader range of activities, including predictive modeling, machine learning, and automation.

Data science is an iterative and evolving field; tangible results can often be observed throughout the project's lifecycle. Initial insight from data science, preliminary model, and incremental improvement provide value along the way, even before the final solution is fully developed. The timeline for tangible results in data science professional services highly depends on each project's unique circumstances and complexities.