Formation Smart Data Science for Automated Analytics of Modeling of Scientific Experiments

Specialists of past generations have accumulated huge and valuable experience of analytics and forecasting in a variety of subject areas. Within the framework of the development of methods of processing scientific data, taking into account the accumulated experience of generations of specialists, it is possible to structure, specify, classify and rank them into flows of smart data for automation of research of various scientific problems by cognitive systems of artificial intelligence. Automated analytics is based on big data. A large amount of data is generated in real time by modeling a scientific experiment. When working with data, it must be processed as efficiently as possible to get the correct output. The main thing is to prepare the training sample correctly. If you select the training data sampling principle correctly, you can scale the task using a more complete set of data. It should be understood that rationing and data preparation is crucial for traditional machine learning. This process significantly affects the choice of the architecture of neural networks used, especially in so-called deep learning, when it is necessary to correctly determine the number of hidden layers in the neural network and the number of artificial neurons in them. One of the main advantages of multilayer neural networks is the simulation of the work of some complex mathematical dependence.