Data Essays

even your personal data that could be sold to third party companies. Major data breaches of popular website skyrocket to the news on what seems to be like on a daily basis, leaking thousands, if not millions of individuals person information. This data breaching can expose your bank account, credit card number, social security number, and many other confidential information that you would not share. The increase of cloud data, which is stored not on your device, but data from a company's server

I am writing in regard to your advertisement for a Data Analyst Intern posted on Indeed.com. Currently, I am a junior majoring in Economics and minoring in Statistics and Business. I believe my academic background coupled with my university involvements perfectly address the needs of the position. As a student at Penn State, I have taken classes that highly correlate with the Data Analyst Intern position. Some of the most relevant courses include… I learned to optimize and apply statistical models

Abstract- Outlier detection is an active area for research in data set mining community. Finding outliers from a collection of patterns is a very well-known problem in data mining. Outlier Detection as a branch of data mining has many applications in data stream analysis and requires more attention. An outlier is a pattern which is dissimilar with respect to the rest of the patterns in the data set. Detecting outliers and analyzing large data sets can lead to discovery of unexpected knowledge in area

Different information literacy and data literacy Information literacy According to author (Koltay, 2015) that state in article in search of a name and identity are information literacy emphasizes critical thinking and the necessity to recognize message quality. It has strong positions among literacies despite some scepticism, highlighting the fact that this concept and especially the lack of information literacy has always seemed to be of more importance to academic librarians than to any other players

spatial or geographical data. FOSS; Free or Open Source Software. FOSS programs have licenses that allow users to freely run the program for any purpose, modify the program as they want, and also to freely distribute copies of either the original version or their own modified version. ILWIS; Integrated Land and Water Information System is a GIS / Remote sensing software for both vector and raster processing. ILWIS features include digitizing, editing, analysis and display of data as well as production

Many business executives ask if Big data is just another fancy alternative to analytics. They are related, but there are a few major differences. Originally, big data was defined by the three V’s, but today it has grown to seven V’s. Let’s discuss each of them in detail. The Original V’s: i. Volume: As of 2012, about 2.5 exabytes of data was created each day, and that number has doubled and will continue every 40 months. More data across the internet every second than were stored in the

Data mining can be viewed as a result of the normal development of information technology Since 1960, database and information technology has been growing methodically from primitive file processing systems to complicated and prevailing database systems [11] [13]. Figure 1.1: History of data base system and data mining Data mining drives its name for searching a important information from a large database to utilize this information in better way. It is, though, a misnomer, as mining for gold

The health care industry can and will benefit greatly from big data. As health care professionals look for ways to reduce disease, treat patients, and lower costs, big data will be heavily used to bridge the gaps. Doctors all around the world will be able to enter endless amounts of data and in return, big data can provide valuable statistical information on specific ailments and what factors contributed to development. Once you factor that in with a specific patient, a doctor will be able to make

Growing, cross-channel data volumes The rise of mobile, tablets and social media has accelerated the growth of available customer data. A typical retailer knows not only the basic demographic information about a customer, but purchase history, call center interaction, mobile/social interaction, supply chain data and more. The sheer volume of information available to retailers is unprecedented, even for brands that have years of experience analyzing customer data. 2. Increasing investment in technology

1.1. DATA MINING Data mining refers to extracting or mining knowledge from large amounts of data. Data mining has attracted a great deal of attention in the information industry and in society as a whole in recent years, due to the wide availability of huge amounts of data and the forthcoming need for turning such data into useful information and knowledge. The information and knowledge gained can be used for applications ranging from market analysis, fraud detection, and customer retention, to

Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness

discovery also known as data mining is the processes involve penetration into tremendous amount of data with the support from computer and web technology for examining the data. Data mining is a process of discovering interesting knowledge by extracting or mining the data fromlarge amount of data and the process of finding correlations or patterns among dozens of fields in large relational databases [3, 4]. Privacy Preserving in Data Publishing (PPDP) is very important in data mining when publishing

analyze healthcare data, make discoveries in different areas, reveal the best solutions for problems and assess the effectiveness s of processes that have already been implemented. Data mining in the healthcare industry is usually the initial step of coming up with predictive analytics, which is a process called data discovery. In many ways, the practice of data mining is similar to predictive analytics since the two concepts use a mathematical approach to break down and analyze data. Crockett and Eliason

imagine you have 10 billion rows of retail SKU data that you are trying to compare. The user trying to view 10 billion plots on the screen will have a hard time seeing so many data points. One way to resolve this is to cluster data into a higher-level view where smaller groups of data become visible. By grouping the data together, or “binning,” you can more effectively visualize the data. 3.5 Dealing with outliers The graphical representations of data made possible by visualization can communicate

Group Assignment a. Discuss the two data mining methodologies The process of going through massive sets of data looking out for unsuspected patterns which can provide us with advantageous information is known as data mining. With data mining, it is more than possible or helping us predict future events or even group populations of people into similar characteristics. Cross Industry Standard Process for Data Mining (CRISP-DM) is a 6-phase model of the entire data mining process which is commonly used

How Big Data Is Beneficial For Businesses Big data is defined as the collection, processing and availability of huge volumes of streaming data in real-time. Most companies have moved big data and analytics to the center of their business. Because when big data and analytics become the logical engine, they impact decisions, fuel interactions and engagement and power up processes and systems of record and everyone gains great insights necessary to respond to the demands of business through decisive

2.2 Data Mining in Authorship Collaboration Nowadays, data mining in authorship collaboration gaining interest and demand among the researchers. Data mining techniques have been applied successfully in many areas from traditional areas such as business and science (Fu, 1997). A lot of organizations now employ data mining as a secret weapon to keep or gain competitive edge. The application of data mining techniques is becoming increasingly important in modern organizations that seek to utilize the

United Parcel Company (UPS) which is a logistics company takes many uses of big data. UPS have been tracking package movement and transaction information since 1980s. Currently the company tracks over 16.3 million packages of 8.8 million customers per day. Company takes up about 16 petabytes of data. Big data gets gathered from sources such as UPS transportation trucks that include truck speed, direction, braking, drive train performance, fuel levels etc. UPS consist of the world’s largest operations

Abstract Big data is everywhere. Big data revolution is creating paths to collect and analyze information of varying sizes, types and volume. It’s not only used in sectors like marketing, sales and product development. The potential use of big data is also spread to HR and Finance which help in finding new insights and strategic decision making. With big data, HR has exceptional opportunities to become more data driven analytical and strategic in the way it obtains talent. Utilizing the power of

Data Science vs Statistics Data science is one of the rapidly emerging trends in computing and is a vast multi-disciplinary area. Data science combines the application of subjects namely computer science, software engineering, mathematics and statistics, programming, economics, and business management. Data science is based on the collection, preparation, analysis, management, visualization and storage of large volumes of information. Data science in simple terms can be understood as having strong