A Study On Data Science

What is big data and how is it defined in today’s modern world? In 2001 Doug Laney Articulated what Big Data meant and this has become the industry standard for the idea. (SAS, 1) More accurately he described the three main attributes that defined Big Data. Laney articulated this idea during a period when Big Data was in its infancy. The amount of Data that could be stored did not allow for the level of analysis that is now common practice fourteen years later. This level of analysis is both changing the way we do business and changing the way we save lives. The amount of data collected from social media alone has completely revolutionized the marketing world allowing for targeted marketing campaigns at certain demographics. Laney described big data in three ways. (SAS, 1)

Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

Data mining is used in variety of fields and applications (Galit, Stiumueli, Natin & Peter 2010). This includes the military for purposes of intelligence,

Big data analytics can help enterprises to better explore and understand the information contained within the data and will also help to recognize the data which becomes critical for future business decisions. Big data analysts basically depends on the knowledge of the analyzed data

What is the definition of big data? What the difference between big data and data? Figuring out these two questions can help us to comprehend big data in depth. Big data has been defined as the data that is too big to be analyzed by traditional methods. Generally speaking, there are five characteristics for big data: data volume, data variety, data velocity, data variability, and data complexity, which were also known as the five dimensions for big data.3 Data volume means that the measurement of the units of data storage. Data variety indicates the diverse forms of data. Data velocity has been defined as the velocity of data producing and processing. Data variability means that the data flow could be inconsistent. Data complexity indicates that the data is hard to analyze. Also, the five dimensions of big data can be seen as the differences between big data and data. Figure 1.13 shows the dimension diagram for big data.

Data science gives data data-based evidence to accept or reject any business decision. It can increase profits and identify potential risks before they actually occur. It can identify potential customers and also provide solutions to otherwise error prone and difficult tasks.

Big data refers to the growing data a company experiences but this data has to be managed and processed to convert it into useful information. For example, a company XYZ just launched an e-commerce website and its receiving a

The “Big Data” becomes a common word now in Information Technology and Business world. These two simple English words created history and meant a ton in Global market. In past the data refers to traditional data and data volumes either in single or multiple terabyte ranges. But today it’s beyond traditional data and includes real time transactional data which is a key to the business systems.

Big data is defined as “large data sets or to systems and solutions developed to manage such large accumulations of data, as well as for the branch of computing devoted to this development.” (“Big Data”) This definition of big data was not added to the dictionary until 2014. The next big thing in business analytics is a relatively new, yet, explosive business practice known as data mining: the collection and analysis of big data. (Fayyad) These large, seemingly random, sets of data are condensed and analyzed for patterns and trends by people with a very broad set of skills. These people are known as data scientists and are considered unicorns in today’s job market.

Presence of big data is a very common phenomenon now days, specially when talking about medium to large size corporation. Manyika et al., in their article (James Manyika, 2011) defined the term big data as “large pools of data that can be captured, communicated, aggregated, stored, and analyzed”. To clarify they suggested that big data refers to data, whose size makes it impossible to be processed by the typical software used for database management. Gartner (Gartner, 2012)defined big data in terms of its characteristics of high volume, high velocity and high variety. By volume, he referred to the size of the data, by velocity he referred to the speed at which the data is created and by variety he referred to the range of types of data.

TITLE A Big Data is fast becoming a ubiquitous term in the world of computers – but what does it actually mean? Explain the fundamental principles of Big Data and discuss the impact it is having, and may continue to have, on modern computing. What challenges does the model bring and in what ways can these be resolved?

Big Data is the term which is used for data sets to make a very large application for processing the data. The Processing may involve analysis, search, sharing, storage, visualization and privacy information. Big data is a type of predictive analysis which is used for the purpose of extract the value from data. The data sets are used for the purpose of analysis to find the new correlation of business trends, for the prevention of diseases and so on.

The paper provides background and related literature on the Big Data, studies the concept from Relational Database to current NoSQL database which have been fueled by the growth Big Data and importance of managing it. And surveys the Big Data challenges from the perspective of its characteristics Volume, Variety and Velocity and attempts to study how those challenges can be addressed.

The phrase Big Data itself is somewhat of an umbrella term, referring to anything from search engine inputs to Facebook posts and Twitter updates, to what the weather was like in Mumbai, India six years ago. By itself, this information may seem pointless, and some of may be inaccurate. However, when coupled with a larger array of information, the data may form a picture of a larger trend. Though some of the data is imperfect, other collected data can be used to check the accuracy of each piece of data. Therefore, it 's a more efficient way of gathering and using data. For example, Kenneth Cukier introduces Shigeomi Koshimizu, a professor at

Data mining is a logical process that is used to search through large amount of data in order to find useful data [2].There are many different types of analysis that can be done in order to retrieve information from big data. Each type of analysis will have a different impact or result. Which type of data mining technique you should use really depends on the type of business problem that you are trying to solve. Different analyses will deliver different outcomes and thus provide different insights [1].