10 key concepts to understand Big Data and Data Science What is Big Data? Big Data refers to humongous volumes of data that cannot be processed effectively with the traditional applications that exist. The processing of Big Data begins with the raw data that isn’t aggregated and is most often impossible to store in the memory of a single computer. A buzzword that is used to describe immense volumes of data, both unstructured and structured, Big Data inundates a business on a day-to-day basis. Big Data is something that can be used to analyze insights that can lead to better decisions and strategic business moves. Join the Big Data training institute in btm to get profound knowledge in the web development tools.
What is Data Science? Dealing with unstructured and structured data, Data Science is a field that comprises everything that related to data cleansing, preparation, and analysis. Data Science is the combination of statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently, and the activity of cleansing, preparing, and aligning the data.
If you are the one who is a hunger to become the certified Pro Tableau Developer? Or the one who is looking for the Data Science Training Institute in Bangalore which offering advanced tutorials and tableau certification course to all the tech enthusiasts who are eager to learn the technology from starting Level to Advanced Level. 1. Artificial Intelligence. Artificial intelligence is colloquially known when a series of cognitive functions typical of human beings, based on reasoning and behavior, are applied to a machine. Another definition involves considering it as a “multidisciplinary area, which, through sciences such as computing, logic and philosophy, studies the creation and design of entities capable of solving everyday problems on their own, using human intelligence as a paradigm. " But what relationship does it have with the discipline of big data? The common denominator, how could it be otherwise, is the data. Artificial Intelligence thrives on huge amounts of data, from which it learns. In such a way that applying statistics, one is able to make predictions for the future. .
2. Machine learning: There is also often confusion between machine learning and artificial intelligence, since, although they go hand in hand, the main difference is the context of subordination that exists between one term and the other. Machine learning is a concept that is framed within artificial intelligence; it is one of its branches of study. Machine learning specifies a way of learning machines from the data entered. It shares the use of algorithms with artificial intelligence, but focuses on "educating" the machine to achieve a greater degree of autonomy. In short, it focuses on the machine self-learning and correcting errors on its own.
3. Deep learning: It is the most complex concept of the three (Artificial Intelligence, Machine Learning and Deep Learning) and the most sophisticated technology of the three, because it takes basic concepts of Artificial Intelligence with the aim of solving problems from deep neural networks that imitate way in which our brain makes decisions . In the Deep learning approach "logical structures are used that are more similar to the organization of the mammalian nervous system, having layers of process units (artificial neurons) that specialize in detecting certain characteristics existing in perceived objects". Deep learning computational models mimic the architectural features of the nervous system, allowing networks of process units within the global system to specialize in detecting certain features hidden in data. Autonomous cars are a clear example of how Deep learning technology is being applied in our daily lives.
4. Algorithmic Models: An algorithm is a prescribed set of instructions or rules that allows an activity to be carried out by successive steps. However, in Artificial Intelligence it is difficult to define algorithms that do what we want: what steps must be followed to detect a cat in a photo? Or to decide if a client is going to change the telephone company? Or to estimate the volume of business that a company will have in the next month? These problems cannot be solved with specific instructions given to a computer. Instead, complex mathematical models are used that learn from examples, that is, through data. This learning process is called model training.
5. Variables: In the Big Data world, variables are the magnitudes included in each of the examples that algorithmic models are nourished by. For example, in the abandonment detection problem, some relevant variables may be: the frequency of use, the contracted volume, whether or not you use the mobile app, etc. All this information is what makes up the data set, or dataset, with which the algorithmic model will be trained.
6. Data mining: This term refers to the action of exploring the data in order to find relationships between variables and behavioral patterns to guide modeling.
7. Clustering: It is related to data mining in the sense that clustering is a technique of the former. The process consists of, through mathematical algorithms and using the information collected from the variables, dividing or classifying the existing data into groups with similar characteristics. Each group is called a cluster, and each cluster is therefore made up of objects that are similar to each other, but different from the objects in other clusters. It is important not to confuse the clustering technique with that of classification, since the first is framed within unsupervised learning (we have information from an input dataset) while the classification is within supervised learning (we have both input and output, that is, each data is already labeled)
8. Natural language processing. It is also related to Artificial Intelligence because PLN (or NLP, Natural Language Processing) is a branch of study of AI. Try to investigate the way in which machines communicate with people
through the use of natural languages such as Spanish or English. Siri, Cortana or Alexa are examples of PLN in our day to day. .
9. Unstructured data: This term is related to the world of data analysis insofar as unstructured data is precisely what has led the big data revolution due to its real-time storage capacity. They are those that are in the format as they were collected. They lack a specific format. They cannot be stored in tables because their information cannot be broken down to more basic types of data. Examples of unstructured data are PDF, emails, images.
10. Data Lake: Most companies have digitized data distributed throughout all areas of the organization, and sometimes it happens that each employee has their data located but not others, and the departments become sealed silos where the data is not transferred. A Data Lake is a shared data environment in its original format that comprises multiple repositories. The Data Lake uses a flat architecture to store the data, that is, the information is stored in a multitude of flat files that are not processed until their use is necessary.