Natural language processing (NLP) is a technology spawned from the need for machines to understand and communicate with humans in human language, not formal computer languages, says a company statement.

Big data has made great inroads into a slew of industries. Healthcare, being one of the big and complex industries in the US, it is ideal for big data initiatives.

Over the recent years, the data capture technology has influenced a large sector of business organizations in terms of novel approaches and seamless execution. Electronic clinical outcome assessments and Electronic patient report assessments follow methodologies that are appropriate for effective data capture mechanisms. Both of these technology-enabled methods project a comprehensive space into user's everyday lives for using their device-inclusive approach i.e. the data captured can be accessed from any internet connected device such as mobile phones, laptops, tablets, medical devices, and PCs.

Tableau enables businesses to make decisions using the data visualization features available to business users of any background and Industry. It empowers businesses to keep up with the continuously evolving technology and outperform its competition through an innovative means of visualizing their data. There is not a single data source that Tableau fails to connect with. Let it be Data Warehouse, MS Excel or any web data, it establishes a connection with all of them. Basically, in any type of data analytics, Tableau provides an end to end insight by transforming data into visually engaging, interactive views in dashboards. With easy to use drag-and-drop interface one can come up with insights in few moments rather than months or years.

Technology is changing the world at an unimaginable speed. It has been the greatest transformation tool in the modern world. From the age of Industrial revolution, the world has changed completely. Today, we cannot imagine our life without technology.

The current drug discovery process needs to shift dramatically in order to meet the needs of both the society and patients in the 21st Century. Artificial Intelligence and machine learning, in particular, present the pharmaceutical industry with a real opportunity to do R&D differently, so that it can operate more efficiently and substantially improve success at the early stages of drug development. There needs to be a fundamental shift in drug discovery and Artificial Intelligence holds the key to bringing the pharmaceutical industry into the 21st Century.

The United States Federal Drug Administration (USFDA) defines Data Integrity as data, which is complete, consistent and accurate. It further defines data integrity as data, which is:

One of the main areas that are trending in the research sectors of Machine Learning and Pattern Recognition is Deep Learning (DL). DL focuses on Machine Learning tools and techniques and applies them in resolving complications which lack human or artificial thoughts and could be achieved in data science. DL is achieved by learning over a cascade of many layers. DL handles many real world complications, such as Machine Translation, Object Recognition, and Localization, Speech Recognition, Image caption generation, Distributed representation for text, Natural Language Processing, Image Classification, etc., with its data-driven representation learning. The traditional computing is facing challenges in dealing with high-dimensional and streaming data, semantic indexing, and scalability of models.

The presence of data preprocessing methods for data mining has been reviewed over the past few years with a lot of high volumes, velocity, and a variety of data that require a new high-performance processing. A large computational infrastructure in big data along with a challenging and time-demanding task is involved to ensure successful data processing and analysis. Approaches in big data comprise of definition, characteristics, and categorization of data preprocessing. There is a huge connection between big data and data preprocessing throughout all families of methods and big data technologies and everything will be examined including developments on different big data framework, such as Hadoop, Spark and Flink and the encouragement in devoting substantial research efforts in some families of data preprocessing methods and applications on new big data learning paradigms.

SAS Enterprise Guide is a point-and-click, menu and wizard-driven tool, which provides fast-track learning for quick data analysis, generates code for productivity and speeds your ability to deploy analyses and forecasts in real time.