Big Data by Ketjona

  3 .       What tools are available for visualisation? Consuming large sets of data isn’t always straightforward. Sometimes, data sets are so large that it’s downright impossible to discern anything useful from them. That’s where data visualizations come in.   Data visualization tools provide data visualization designers with an easier way to create visual representations of large data sets.   There are dozens, of applications, tools, and scripts available to create visualizations of large data sets.   Tableau (and Tableau Public)   Tableau has a variety of options available, including a desktop app, server and hosted online versions, and a free public option.   Infogram   Infogram is a fully-featured drag-and-drop visualization tool that allows even non-designers to create effective visualizations of data for marketing reports, infographics, social media posts, maps, dashboards, and more.   ChartBlocks   ChartBlocks claims that data can be imported from “anywhere” using their API, in

  2 .       What tools are available for data mining?   Data mining is looking for hidden, valid, and all the possible useful patterns in large size data sets. Data Mining is a technique which helps you to discover unsuspected/undiscovered relationships amongst the data for business gains.   SAS Data mining:   Statistical Analysis System is a product of SAS. It was developed for analytics and data management. It is one of the best data mining programs which offers a graphical UI for non technical users.   Features:   SAS Data mining tools help you to analyze Big data It is an ideal tool for Data mining, text mining & optimization. SAS offers distributed memory processing architecture which is highly scalable   Teradata:   Teradata is a massively parallel open processing system for developing large-scale data warehousing applications. Teradata can run on Unix/Linux/Windows server platform.   Features:   Teradata Optimizer can handle up to 64 joins in a query. Tera data has a low tot

              1.       What types of problems would you use big data analysis for? Data science dares to ask further questions, looking at unstructured “big data” derived from millions of sources and nontraditional mediums such as text, video, and images. This allows companies to make better decisions based on its customer data.  Here, we look at two real-world examples of how data science drives business innovation across various industries and solves complex problems.                      Data science revolutionizes sports analytics.   Over the past few years, the Strategic Innovations Group at the consulting firm Booz Allen Hamilton has been doing just that — working to transform the way teams utilise data.  Using data science and machine learning tactics, Booz Allen’s team developed an application for MLB coaches to predict any pitcher’s throw with up to 75% accuracy, changing the way that teams prepare for a game. Looking at all pitchers who had thrown more than 1,000 pitches, th

  3 .   How can we limit the negative effects of big data?   (better laws/education etc.?)   General Data Protection Regulation, Europe’s biggest change to data protection in a very long time. If companies through this law are ever to mishandle customer data, the company will be given a strict penalty because of such misuse.  Clear consent must be given from users of websites and companies online before their information is used, it’s a way to make sure that user information is safe and that companies are completely transparent with how they handle and use user information and data. S ome other ways to limit the risks of big data are: Understanding the risks: One of the best ways to protect against big data security threats is to understand the risks and implement measures to reduce potential incidents.  Eliminate unneeded data:  Many companies stockpile all of their data, but some of this can be jettisoned once the organization identifies which information is the most useful.  Strengt

  2.    What are the advantages and disadvantages of big data for society? Big data is growing in a number of industries, and healthcare is no exception.  There are many positive ways to use big data in a personalized healthcare. The program may sound powerful, but it doesn’t come without risks. While big data has many advantages, the disadvantages should also be considered before making the jump. Advantages: Higher-Quality Care Because big data draws from a number of sources, including previous doctor and pharmacy visits, social media, and other outside sources, it can create a more complete picture of a patient. Similarly, a doctor may be able to see underlying causes for a health issue that wouldn’t be easily visible with just basic health information. Early Intervention The overall goal of big data in healthcare is to use predictive analysis to find and address medical issues before they turn into larger problems. Big data definitely makes the entire process more efficient.   Disad

                           1. What are the advantages and disadvantages of big data for the individual? The term “big data” refers to the recording, storage, and processing of information related technologies. Big data does not necessarily define data sets and databases: it refers to knowledge processing systems, methods, and tools. The revolutionary nature of a real-time data processing system enables users to be given immediate feedback without having to wait for further study. Pros of Big Data for individuals:   ·         Detects Fraud  Another important advantage companies find with big data is that it can help identify fraud. That benefit is so profound from a financial services perspective that the identification always takes place before the consumer really realizes something is wrong. ·         Customer Service One of it’s most-cited goals of a big data implementation effort is to improve the interactions between customers. AI, machine learning, and similar systems can analyze