Business Finance
DAT565 Phoenix Predictive Analytics and Relationship to Data Mining Paper

DAT565

University of Phoenix

Question Description

Analyze data mining in a minimum 1,400-word report and include the following:

  • Explain the importance of data mining, and examine its implementation in real-world scenarios.
  • Summarize the misconceptions of data mining.
  • Critique the confusion of data mining with Online Analytical Processing (OLAP).
  • Analyze the importance of statistical analysis and machine learning techniques in data mining.

Cite a minimum of 1 reference from Chapter 2 of the textbook assigned for this course.

Unformatted Attachment Preview

Predictive Analytics and Relationship to Data Mining Grading Guide DAT/565 Version 1 Individual Assignment: Predictive Analytics and Relationship to Data Mining Purpose of Assignment Business Analytics has evolved from providing descriptive outcomes based on historical information to helping predict future activities from potential customers or groups leveraging that same information. In order to accomplish it, new techniques have been implemented for mining the information and thus expanding to modeling and predictive analytics. Resources Required REAL-WORLD DATA MINING: APPLIED BUSINESS ANALYTICS AND DECISION MAKING. Dursun Delen, (2015). Chapter 2: Introduction to Data Mining Grading Guide Content Met Partially Met Not Met Comments: Explain the importance of data mining, and examine its implementation in real-world scenarios. 2 Summarize the misconceptions of data mining. 2 Critique the confusion of data mining with Online Analytical Processing (OLAP). 1 Analyze the importance of statistical analysis and machine learning techniques in data mining. 2 The paper is a minimum of 1,400 words in length. 1 Writing Guidelines Met Total Available Total Earned 8 #/8 Partially Met Not Met Comments: The paper — including tables and graphs, headings, a title page, and a reference page — is consistent with APA formatting guidelines and meets course-level requirements. .4 The paper includes properly cited intellectual property using APA style in-text citations and a reference page. .4 The paper includes paragraph and sentence transitions that are logical and maintain flow throughout the paper. .4 The paper includes sentences that are complete, clear, and concise. .4 Copyright © 2015 by University of Phoenix. All rights reserved. 1 Predictive Analytics and Relationship to Data Mining Grading Guide DAT/565 Version 1 The paper follows proper rules of grammar and usage including spelling and punctuation. Assignment Total .4 # Total Available Total Earned 2 #/2 10 #/10 Additional comments: Copyright © 2015 by University of Phoenix. All rights reserved. 2 Predictive Analytics and Relationship to Data Mining Grading Guide DAT/565 Version 1 Individual Assignment: Predictive Analytics and Relationship to Data Mining Purpose of Assignment Business Analytics has evolved from providing descriptive outcomes based on historical information to helping predict future activities from potential customers or groups leveraging that same information. In order to accomplish it, new techniques have been implemented for mining the information and thus expanding to modeling and predictive analytics. Resources Required REAL-WORLD DATA MINING: APPLIED BUSINESS ANALYTICS AND DECISION MAKING. Dursun Delen, (2015). Chapter 2: Introduction to Data Mining Grading Guide Content Met Partially Met Not Met Comments: Explain the importance of data mining, and examine its implementation in real-world scenarios. 2 Summarize the misconceptions of data mining. 2 Critique the confusion of data mining with Online Analytical Processing (OLAP). 1 Analyze the importance of statistical analysis and machine learning techniques in data mining. 2 The paper is a minimum of 1,400 words in length. 1 Writing Guidelines Met Total Available Total Earned 8 #/8 Partially Met Not Met Comments: The paper — including tables and graphs, headings, a title page, and a reference page — is consistent with APA formatting guidelines and meets course-level requirements. .4 The paper includes properly cited intellectual property using APA style in-text citations and a reference page. .4 The paper includes paragraph and sentence transitions that are logical and maintain flow throughout the paper. .4 The paper includes sentences that are complete, clear, and concise. .4 Copyright © 2015 by University of Phoenix. All rights reserved. 1 Predictive Analytics and Relationship to Data Mining Grading Guide DAT/565 Version 1 The paper follows proper rules of grammar and usage including spelling and punctuation. Assignment Total .4 # Total Available Total Earned 2 #/2 10 #/10 Additional comments: Copyright © 2015 by University of Phoenix. All rights reserved. 2 ...
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Final Answer

Attached.

Predictive Analytics and Relationship to Data Mining
Thesis statement: Enterprise's managements employ the concept of data mining in the desire of
understanding various business aspects from the analysis of organizational pattern generated
form the extracted information.
I.
II.

Importance of Data Mining
Application of Data Mining in Real-world Scenarios

III.

Misconceptions of Data Mining

IV.

The confusion of Data Mining and Online Analytical Processing (OLAP)

V.
VI.

Importance of Statistical Analysis
Importance of Machine Learning Technique in Data Mining


Running head: DATA MINING

1

Predictive Analytics and Relationship to Data Mining
Student’s Name
Institutional Affiliation

DATA MINING

2
Predictive Analytics and Relationship to Data Mining

Data mining is a concept used in enterprises that entails the conversion of bulk raw data
into useful information that could provide different insight essential in making strategic decisions
in an organization (Roiger, 2017). The processes involve the utilization of various software
applications to extract important information and analyze diverse trends, for instance, concerning
a company production process, in course of developing plans as well as strategic goals. Often,
data mining enhances in predictive analytics aimed at providing solutions to the enterprise's
problems in various scenarios such as revenue prediction, stock markets, healthcare, and sports.
Nonetheless, the outcomes from a data mining process are heavily dependent on the data
collection process, data warehousing and the analyst technical know-how. Enterprise's
managements employ the concept of data mining in the desire of understanding various business
aspects from the analysis of organizational pattern generated form the extracted information.
Importance of Data Mining
Today, data mining is an important procedure in many business areas in the course of
running an enterprise, following the growing competition in the business environment (Larose,
2014). One of the importance of data mining entails the analysis of business patterns that assist
in the formulation of variant tactical enterprise goals. Through the generated patterns in data
mining procedures, a company is able to carry out a behavioral evaluation aimed at streamlining
various operations and organizational processes. The second importance of data mining is the
development of analytical insights that assist enterprises in enhancing profitability by
maximizing resources use and in the overall business development. Besides, data mining
proc...

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