Data Analysis : Data Mining Essay

Data collection has been around for years in one form or another. The implementation of the No Child Left Behind Act stimulated dedicated educators to learn the correlation between data driven decision-making and successful school improvement plans. The legislative goal was to ensure academic success across all socioeconomic frontiers. Districts across the country were steered into driving their instruction with data and teacher collaboration. This has lead to districts that have successfully found the correlation between data driven decision-making and success.

“If a Bag is purchased, a Blush is also purchased at that same transaction.” (“If Bag, then Blush.”) While Bag is antecedent, Blush represents consequent.

Data Mining. It is the process of discovering interesting knowledge that are gathered and significant structures from large amounts of data stored in data warehouse or other information storage.

After careful data analysis, the need for instructional improvement on strategies to address the needs of English language learners it is apparent. Improving literacy skills is critical in decreasing achievements gaps of this subgroup. As mentioned previously, Pinewood maintains a school grade of a B; nevertheless, a focus on strategies to meet the needs of ELL students will benefit all students. With effort from all stakeholders, a focus on instructional strategies to improve learning of English learners could result in decreasing the achievement gap as well as increasing the school grade to an A, since many of these students count for the lowest 25%. Correspondingly, Marchand-Martella, Klingner, and Martella (2010) justify that

Data Mining is an analytical process that primarily involves searching through vast amounts of data to spot useful, but initially undiscovered, patterns. The data mining process typically involves three major steps—exploration, model building and validation and finally, deployment.

What is data mining? Data mining is the deriving new information from massive amounts of data in databases (Sauter, 2014, p. 148). Chowdhurry argues that data mining is part of KDD. KDD is knowledge discovery in databases, it is a process that includes data mining. In addition to data mining, KDD includes data preparation, modeling and evaluation of KDD. KDD is at the heart of this research field. This research field is multidisciplinary and includes data visualization, machine learning, database technology, expert systems and statistics. Overall, the use of a case based reasoning and data mining tools within an information system would create a CBR system to solve new problems with adapted solutions and could be used in many industries such as education and healthcare (Chowdhurry,

Data mining is a class of database applications that looks for hidden patterns in a group of data that can be

DATA MINING: means searching and analyzing large masses of data to discover patterns and develop new information.

The need to find a way to handle big data leads to data mining. Most researchers defined data mining similarly to Swain (2016). Swain described data mining as using technological processes to analyze big data and find unsuspected relationships among variables for future use. He continues to state that data mining technologies can find value from billions of gigabytes of data gathered from various sources. Huang, Lu, & Duan (2012) add to Swain’s definition by noting that, as opposed to typical statistical studies, data mining uses computational methods that allow the study to look

With the increased and widespread use of technologies, interest in data mining has increased rapidly. Companies are now utilized data mining techniques to exam their database looking for trends, relationships, and outcomes to enhance their overall operations and discover new patterns that may allow them to better serve their customers. Data mining provides numerous benefits to businesses, government, society as well as individual persons. However, like many technologies, there are negative things that caused by data mining such as invasion of privacy right. This paper tries to explore the advantages as well as the disadvantages of data mining. In addition, the ethical and global issues regarding the use of data mining

This report is divided into three task in context to data analysis and data mining. The first task consist of rapid miner and it uses data analysis in order to get the details of the customer. The first part explains the factors effecting the deliquesces. This analysis helps in understanding the data of customer. After all this analysis is done then exploratory analysis is done this is done using rapid miner. This variable are used for making decision tree and logistic regression model which gives the analyst a predictor variables. The next step is making a report on data warehouse and security concerns around it. The last step is tableau software which is being used to manufacturing for San francisco police department.

Abstract— Data Mining extracts useful information about data. In other words, Data Mining extracts the knowledge or interesting information from large set of structured data that are from different sources. Data mining applications are used in a range of areas such as it is used for financial data analysis, retail and telecommunication industries, banking, health care and medicine. In health care, the data mining is mainly used for disease prediction. In data mining, there are several techniques have been developed and used for predicting the diseases that includes data preprocessing, classification, clustering, association rules and sequential patterns. This paper analyses the performance of two classification techniques such as Bayesian

Data Warehousing and Data Mining has always been associated with manufacturing companies, where sales and profit is the main driving force. Subsequently Higher Education has grown throughout the years; this growth is predominately associated with the increase of online institutions. This growth has resulted in higher education to adapt to a more business like institution (Lazerson, 2000).

Data has always been analyzed within companies and used to help benefit the future of businesses. However, the evolution of how the data stored, combined, analyzed and used to predict the pattern and tendencies of consumers has evolved as technology has seen numerous advancements throughout the past century. In the 1900s databases began as “computer hard disks” and in 1965, after many other discoveries including voice recognition, “the US Government plans the world’s first data center to store 742 million tax returns and 175 million sets of fingerprints on magnetic tape.” The evolution of data and how it evolved into forming large databases continues in 1991 when the internet began to pop up and “digital storage became more cost effective than paper. And with the constant increase of the data supplied digitally, Hadoop was created in 2005 and from that point forward there was “14.7 Exabytes of new information are produced this year" and this number is rapidly increasing with a lot of mobile devices the people in our society have today (Marr). The evolution of the internet and then the expansion of the number of mobile devices society has access to today led data to evolve and companies now need large central Database management systems in order to run an efficient and a successful business.

The aim of this chapter is focus on how data required for the research is to be obtained. Section 3.2 outlines the sampling techniques that can be used to choose representative respondents to the population under study. It will give a brief explanation on sampling and also explanation on various sampling techniques. Section 3.3 focuses on the data collection methods. It will give a brief explanation of various data collection methods. Section 3.4 describes data analysis. It explains how data collected will be analysed. Section 3.5 will give an outlook on data analysing and section 3.6 describe on data presentation.