Data Science

Cognitive computing describes those technological platforms that are especially based on scientific disciplines of signal processing and artificial intelligence . These platforms include machine learning, reasoning, object recognition, speech recognition, and natural language processing. In the present day, cognitive computing is referred to as new software and hardware that can mimic the functioning of the human brain and can improve decision making. Cognitive computing connects data adaptive page displays and data analysis to adjust the contents for a specific kind of audience. In this article, I will discuss the features and applications of cognitive computing. Features and Use Cases Cognitive computing is a modern form of computing with the objective of creating accurate models about human brain senses, reasons and also respond to different stimulus. Cognitive computing has some features. Cognitive computing is adaptive ; it learns the changes in the information and ad

INTRODUCTION In data science technology, there are two techniques which are ruling the industry at the present time. We have frequently heard their names that are artificial intelligence and machine learning. Artificial intelligence is not technology, but it is a study of making the machines intelligent. We can say that machine learning is an application of artificial intelligence . Two concepts which are majorly used in artificial intelligence are neural networks and game theory. The neural network is a whole different concept from the game theory. WHAT IS GAME THEORY? Game theory is nothing but a branch of mathematics in which the concept of mathematics is implemented for the computation and implementation of games. At the present time, game theory is playing an important role in artificial intelligence. It is very obvious that the perfect application of the concepts of game theory is the games. It has been noticed that the techniques, artificial intelligence and game theo

INTRODUCTION At the present time, the size of the data has increased very rapidly. With the increased size of the data, a number of patterns, dimensions, different factors have also increased. When we store and represent such a huge amount of data, then it looks very difficult and tricky to understand. For getting accurate, effective and productive output from the data, a data analyst has to understand the data very clearly. Here, we understood why it is important to understand the data very clearly. For solving this problem of the large set of data which is often called as big data, the concept of segmentation is used. WHAT IS SEGMENTATION? Segmentation is a collection of all the techniques which are used to reduce the complexity of the data. Here complexity means a greater number of patterns, factors as said above. Here are the techniques which pile up the whole segmentation. They are listed below--> ·          Clustering ·          Decision trees ·        

WHAT IS ASSOCIATION ANALYSIS? Data science technology  is the most influential technology of the present time. The first step of this technology is analyzing the data. One of them is the association analysis. Association analysis is used to recognize hidden relationships between the data. The relationships between the data are very useful. Data analysts can extract any information from it. The relationships which are recognized from the data are expressed as the compilation of association rules. Association rules do not find the relationships between individuals, it recognizes relationships among the group of the people. For understanding association analysis and association rules better, we will cite an example of a supermarket where all the relevant items are grouped together. COMPONENTS OF ASSOCIATION RULES There are two components of association rules. They are listed below--> • Antecedent • Consequent Antecedent and consequent are separate lists of the items (kept in a sup