In Units Three, Four, and Five, students used data charts that displayed several columns of information, to include ‘Valid Percent’ and ‘Cumulative Percent.’ For this discussion post, students will explain the difference between the two types of percents. Why are these statistical elements important to the data presented? Explain your rationale with a minimum of 250 words, supporting your discussion with two scholarly references and in-text citations. Respond to this question with a minimum of 250 words and two scholarly sources from the Library For more information on Valid Percent read this: https://en.wikipedia.org/w/index.php?search=Valid+Percent&title=Special:Search&go=Go&ns0=1
Technology and Enterprise Resource Planning As an IT manager, discuss how your company will use Enterprise Resource Planning (ERP) to integrate the various functions of an entity. What are the advantages of using ERP? In your discussion, please be sure to provide a substantive explanation of what ERP is and give example(s) of ERP. Use APA throughout. Your main discussion should be at least 300 words or more. For more information read this:
General Requirements: Use the following information to ensure successful completion of the assignment: Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center. This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion. You are required to submit this assignment to LopesWrite. Refer to the LopesWrite Technical Support articles for assistance. Directions: Write a paper (1,500-1,750 words, not including title page and references) that addresses the following. The paper should begin with an executive summary of no more than one page. Structure your paper such that individual section titles are created to address each of the following topics: Introduction Analytics Methods and Tools Employed in Data Analytics. Spotlight: One large enterprise usage of analytic methods What methods are used? Are there quantified benefits? Spotlight: One smaller enterprise usage of analytic methods What methods are used? Are there quantified benefits? Applicability of Management Theories: Do identified management theories still work or should they be extended or modified to accommodate evolving business climate characterized with extensive use of data analytics? For more information read this: https://simple.wikipedia.org/wiki/Synthesis
Discuss the importance of regular expressions in data analytics. As part of your discussion please include a specific use case for which a regular expression could be used with a dataset. Also, discuss the differences between the types of regular expressions. (2-3 paragraphs) Choose two types of regular expressions… For example, [brackets] (Matches the enclosed characters in any order anywhere in a string) and * wildcards (Matches the preceding character 0 or more times) and discuss the differences between the two. Please be sure to include two or three differences for each. Include how they help manipulate data (1-2 paragraphs). For more information read this: https://en.wikipedia.org/wiki/Regular_expression
In the Student Assignment File Week 1 tab, complete the problems, and submit your work in an Excel document. See Where Is Help Button in Microsoft Excel 2007, 2010, 2013 and 2016 (Links to an external site.), Descriptive Statistics (Links to an external site.), Load the Analysis ToolPak (Links to an external site.), and Use the Analysis ToolPak to Perform Complex Data Analysis (Links to an external site.) for more information on how to use the required technologies for the course. Be sure to show all of your work and clearly label all calculations.https://www.excel-easy.com/examples/descriptive-statistics.html. For more information read this:https://en.wikipedia.org/wiki/Problem_set
It is the aim of data analytics to produce accurate predictions that are of great value to clients or constituents. Sometimes however these predictions turn out to be wrong for a variety of reasons. Can you think of a case where data was analyzed and a prediction made that turned out to be a colossal mistake?
To begin this process, please do a web search and either read an article or watch a video describing your Google Analytics. The search can be as simple as “What is Google Analytics?” Then watch the following video that demonstrates how to create a Google Analytics account. Then, take the time to watch this video so you have a basic understanding of Google Analytics as a strategic decision tool.