Data Science also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured,similar to data mining. It is the art of extracting meaning from data. The meaning can then be used for a decision-making process. The process includes gathering data, processing it, and using tools for extracting significant information from it. Data Science Training helps you master data analytics, acquisition, statistical methods, machine learning algorithms and deploying R statistical computing.
Python is a widely used high-level programming language for general-purpose programming, created by Guido van Rossum. An interpreted language, Python has a design philosophy that emphasizes code readability and a syntax that allows programmers to express concepts in fewer lines of code.Python features a dynamic type system and automatic memory management and supports multiple programming paradigms, including object-oriented, imperative, functional programming, and procedural styles. It has a large and comprehensive standard library.
R is an open source programming language and software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing.It is widely used among statisticians and data miners for developing statistical software and data analysis.Polls, surveys of data miners, and studies of scholarly literature databases show that R’s popularity has increased substantially in recent years.
Spark was introduced by Apache Software Foundation for speeding up the Hadoop computational computing software process.Apache Spark is a lightning-fast cluster computing technology, designed for fast computation.It is not a modified version of Hadoop and is not, really, dependent on Hadoop because it has its own cluster management. Hadoop is just one of the ways to implement Spark.
Machine Learning is the subfield of computer science that, gives “computers the ability to learn without being explicitly programmed”.It explores the study and construction of algorithms that can learn from and make predictions on data– such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach,optical character recognition (OCR),learning to rank, and computer vision.
Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. It is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text.
Artificial Intelligence also known as machine intelligence, is the intelligence exhibited by machines, rather than humans or other animals (natural intelligence, NI). In computer science, the field of AI research defines itself as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of success at some goal.Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.