...Influence of Gender 4 4.2 – Influence of Single or Double Degree 5 4.3 – Influence of Quiz Results 6 4.4 – Influence of Report results 1 5.0 – Conclusion 1 Bibliography 2 Appendix 2 1.0 – Introduction The purpose of the following report is to display the significant role and factors that affect the student results and to prove the most efficient causes for high marks amongst the 705 students in BSB123. The variables are gender, whether the student is studying a double or single degree, quiz, report and exam results. The scope of the report is the population of university students studying BSB123, which will be examined. 2.0 – Outliers (Refer to the appendix) An outlier is an extreme value that differs greatly from other values that does not follow the overall data pattern. Outliers are beneficial when analysing data, as it helps gather additional information, displays trends and patterns and helps define the scope of the population being investigated. In the provided data, there were no outliers found. Figure 13.0 displays a minimum Z-score of -1.8970567 and a maximum of 2.459087745, meaning no z-scores fell outside the range. This shows that the chosen population is more controlled, less susceptible to variation and that exceptional cases do not vary the data results. 3.0 – Distribution of Exam Results This section of the report will investigate the performance of the students in regards to the final examination. Figure 1.0 – Histogram...
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...SKETCH OF SPECIALIZATION REPORT & EVALUATION FORMATS - 2014 S.No Content Page No. 1 Sketch of Report on Specialization Project 2-3 2 Comprehensive comparative table (Review of Literature) - Annexure -I 4 3 Sequence of the Report- Annexure-II 5 4 Evaluation weightage-Annexure -III 5 5 EVALUATION FORMAT- SP-2014 - At the end of 1st Week 6-7 6 EVALUATION FORMAT - SP-2014 - At the end of 2nd Week 7-8 7 EVALUATION FORMAT - SP-2014 - At the end of 3rd Week 8-9 8 EVALUATION FORMAT - SP -2014 - At the end of 4th Week 9-10 Sketch of Report on Specialization Project Objective The objective of Specialization Project is to provide of in depth learning in the area of specialization. This project is more focused on the students chosen area as a specialization and wants to have career. It should facilitate the student to have a deeper understanding in his/her specialization. Details of Specialization Project The Specialization project should be done by every student as a part of their course which carries 100 marks and will be included in the marks sheet. This project provides an opportunity to get more insights on the topic chosen by the student. In the process, the student will get a chance to (a) evaluate the applicability of the textbook theories in real time situations and (b) to analyze the performance of entities and markets using statistical and analytical methods...
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...Top Data Management Terms to Know Fifteen essential definitions you need to know Fifteen Essential Data Management Terms We know it’s not always easy to keep up-to-date Contents with the latest data management terms. That’s why we have put together the top fifteen terms and definitions that you and your peers need to know. OLAP (online analytical processing) Star schema What is OLAP (online analytical processing) Fact table OLAP (online analytical processing) is computer processing that enables a Big data analytics Data modeling Ad hoc analysis user to easily and selectively extract and view data from different points of view. For example, a user can request that data be analyzed to display a spreadsheet showing all of a company's beach ball products sold in Florida in the month of July, compare revenue figures with those for the same products in September, and then see a comparison of other product sales in Data visualization Extract, transform, load (ETL) Florida in the same time period. To facilitate this kind of analysis, OLAP data is stored in a multidimensional database. Whereas a relational database can be thought of as two-dimensional, a multidimensional database considers each data attribute (such as product, geographic sales region, and time Association rules (in data mining) Relational database period) as a separate "dimension." OLAP software can locate the intersection of dimensions (all products sold in the...
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...Spend Analysis & Data Analysis Robert Knieriem BUS 307 Professor Ray May 21, 2012 In today’s fast paced, technology heavy corporate world, data that pertains to any and every business aspect, regardless of size has become ever more important in daily operations and decision making. The data that results from these operations and decisions and its collection, analysis and concluding into upcoming decisions will have a deciding factor in how well or how poorly the company operates in the future. This data is made on its own, but putting it together and making sense of it all remains the challenge today’s companies have and continue to deal with. Data analysis and critical thinking skills are important to spend analysis because this information put out requires a human element to understand what is really being put out. As mentioned by Purchasing, data is only data until it’s analyzed and acted on, and only then does it become valuable information to the company. Further, today’s companies who actively collect data are finding themselves looking for those with the knowledge and skill to analyze this data and allow the company to act upon it (Bozarth & Handfield, 2008). Even with today’s most powerful software which simplifies the task of collecting and maintaining this data, the process of analyzing and understanding of this data requires the human touch and only someone with this background and knowledge can put the data into easy to read, easy to understand...
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...the true value of information and how it can affect the bottom line. The majority of successful organizations will employ business analysis staff to ensure that the data they get is properly interpreted to help support business-critical choices and strategies. By making use of the swathes of data available about any number of metrics within a company, clear and relevant insight into issues can be translated into more effective decisions and outcomes, based on verifiable knowledge. But these decisions often need to be taken quickly, across multiple departments and divisions, and this need for agility is only going to increase. Customers are only one half of the key to the survival and eventual prosperity of any business, and so it is to the data they provide which organizations must turn if they are to uncover the key themes which require immediate action. Genuinely actionable customer intelligence is the other side of the equation – information which metaphorically ‘gathers dust on a shelf’ is completely pointless. Gathering data must be done in conjunction with a proper strategy for analysis in mind, otherwise it simply becomes a number-gathering exercise in futility. In this paper, we look at the opportunities and limitations offered by current models of data analysis, and how adopting a strategy of combining both statistical and contextual analysis can provide the extra edge needed in an environment where decisions must be made quickly. READ ON... Teneo Insight Delivering...
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...proposal. Now you have the major task ahead of fully analysing the data you brought with you from the field and writing your research report. The report should contain feasible and useful recommendations, based on the findings of your study concerning how to solve the problem investigated. As in the first workshop there will be presentations, group work sessions and a few plenaries. In this workshop, however, group work will take up most of the time. The presentations will be concentrated in the first week, which will be devoted to data analysis. The second week will be fully reserved for report writing, with only two presentations to guide you. Toward the end of that week an important plenary is planned in which each group will present a summary of its main findings and recommendations. A selected group of policy-makers and health managers who requested the study or have a direct interest in the topic and some interested researchers will be invited to comment on your presentation during that plenary. The modules for this workshop cover several major tasks, which are schematically presented in the diagram on the next page of this module. This diagram is presented again at the beginning of each subsequent module, to indicate which task is the focus of the presentation. We will now briefly introduce each of these tasks. Although we trust that all groups have put great effort in the processing of their data, some adjustment and elaboration may be required. This is normal...
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...Monitoring and Evaluation and Management Information Systems (MIS) 2/ MIS, Monitoring & Evaluation Introduction Monitoring and Evaluation Process points: Types and levels of monitoring and evaluation activities Some monitoring and evaluation definitions What are indicators? Methods and tools for monitoring and evaluation Steps in planning and conducting monitoring and evaluation ❐ Tools and techniques: How to write your evaluation report Tips for improving monitoring and evaluation of your project Management Information Systems (MIS) ❐ What is on MIS? ❐ Why is an MIS important? Sources for decision making, monitoring, and evaluation ❐ Targets Designing an MIS Using data to inform your MIS: conducting a baseline study Performance information Key questions in designing an MIS Institutionalizing an MIS in an organization MIS: step-by-step Using MIS outputs for decision-making How is information used in the organization? ❐ How does information flow in your organization? A word about reporting ❐ Tips and Tools: Report preparation checklist A final word about evaluation ❐ Process evaluation ❐ Output evaluation ❐ Effects evaluation ❐ Short-term impact evaluation Summary 1 2 4 5 5 7 8 9 10 1 1 1 1 13 1 2 1 3 1 5 16 1 7 19 20 2 1 22 25 26 27 Figures, Tables, and Exercises Figure 1: Exercise A: Table 1: Exercise B: Figure 2: Figure 3: Exercise C: Figure 4: Relation between program monitoring and evaluation...
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... except by a reviewer who may quote brief passages in a review. ACL 9 August 2006 ACL Services Ltd. 1550 Alberni Street Vancouver, BC Canada V6G 1A5 Telephone: +1-604-669-4225 E-mail: info@acl.com Web: www.acl.com Printed in Canada ACL, the ACL logo, the ACL logo with the text, "ACL Data you can trust. Results you can see." and Audit Command Language are trademarks or registered trademarks of ACL Services Ltd. Microsoft, Windows and Windows Server are trademarks or registered trademarks of Microsoft Corporation. AIX, OS/390, OS/400 and z/OS are registered trademarks of IBM Corporation. Linux is a registered trademark of Linus Torvalds. SAP, R/2 and R/3 are trademarks or registered trademarks of SAP AG. Crystal Reports is a trademark or registered trademark of Business Objects SA. All other trademarks are the property of their respective owners. 200607031748 C ONTENTS Chapter 1: Take your first look at ACL . . . . . . . . . . . . . . . . . . . . . 1 Making effective business decisions ................................................ 2 What can I use ACL for? ................................................................... 3 How does ACL read data? ................................................................ 4 How do I apply ACL to my project? ................................................ 5 Organizing your project items ......................................................... 6 Viewing and modifying your tables .......................
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...2014 Data Science Salary Survey Tools, Trends, What Pays (and What Doesn’t) for Data Professionals John King & Roger Magoulas Take the Data Science Salary and Tools Survey As data analysts and engineers—as professionals who like nothing better than petabytes of rich data—we find ourselves in a strange spot: We know very little about ourselves. But that’s changing. This salary and tools survey is the second in an annual series. To keep the insights flowing, we need one thing: People like you to take the survey. Anonymous and secure, the survey will continue to provide insight into the demographics, work environments, tools, and compensation of practitioners in our field. We hope you’ll consider it a civic service. We hope you’ll participate today. Make Data Work strataconf.com Presented by O’Reilly and Cloudera, Strata + Hadoop World is where cutting-edge data science and new business fundamentals intersect— and merge. n n n Learn business applications of data technologies Develop new skills through trainings and in-depth tutorials Connect with an international community of thousands who work with data Job # 15420 2014 Data Science Salary Survey Tools, Trends, What Pays (and What Doesn’t) for Data Professionals John King and Roger Magoulas 2014 Data Science Salary Survey by John King and Roger Magoulas The authors gratefully acknowledge the contribution of Owen S. Robbins and Benchmark Research Technologies...
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...HCS/465 October 3, 2013 Results The procedure of collecting data was appropriate because the data was collected in the most efficient and logical way of collecting data for obtaining the desired outcome. The data for this study was collected by a web-enabled database for error reporting system in a hospital setting. Nurses, physicians, and pharmacists had access to the system at any time to report any type of medication error. The rights of the subjects were protected because the patients information was never used on the error reporting database and the error reports made by the staff were anonymous, other than tracking what job title the error reporter held. Reporting any error was completely voluntary to the staff and staff was fully aware of what the system was for. The data collection tool in this instance was a web-enabled database for error reporting. This system supports the reliability of the study by keeping accurate percentage tallies of the types of errors, who is reporting the errors, and the suggestions to prevent the errors. Data Analysis Procedures The data analysis procedures in this study were appropriate for the data collected because the procedures used to analyze the data were as follows: two experts, the investigator, and the pharmacist worked together to analyze the material. The two experts agreed on the categories of errors and the one year time limit on data collection. The data analysis procedures are appropriate for answering the research question...
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...Background In early January 2006, Littlefield Technologies (LT) opened its first and only factory to produce its newly developed Digital Satellite System (DSS) receivers. Littlefield Technologies mainly sells to retailers and small manufacturers using the DSS’s in more complex products. Littlefield Technologies charges a premium and competes by promising to ship a receiver within 24 hours of receiving the order, or the customer will receive a rebate based on the delay. The product lifetime of many high-tech electronic products is short, and the DSS receiver is no exception. LT managers have decided that, after 268 days of operation, the plant will cease producing the DSS receiver, retool the factory, and sell any remaining inventories. As this is a short life-cycle product, managers expect that demand during the 268 day period will grow as customers discover the product, eventually level out, and then decline. In the initial months, demand is expected to grow at a roughly linear rate. Demand is then expected to stabilize. Eventually, demand should begin to decline at a roughly linear rate. Although orders arrive randomly to LT, management expects that, on average, demand will follow the trends outlined above. Management’s main concern is managing the capacity of the factory in response to the complex demand pattern. Delays resulting from insufficient capacity undermine LT’s promised lead times and ultimately force LT to turn away orders. In particular, if an order’s...
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...Information Assurance and Security 14 2.6 Information Assurance and Security 15 2.7 Green IT from Information Assurance viewpoint 16 2.8 Dimension of Green IT 18 2.9 Green IT Initiatives 19 3 Introduction 21 3.1 Research design 21 3.2 Justification of paradigm and methodology 22 3.3 Data Collection Methods 23 3.3.1 Questionnaires 23 3.3.2 Interview 24 3.4 Data Analysis and Interpretation 25 3.5 Ethical Considerations 25 3.6 Chapter Summary 26 4 Introduction 27 4.1 Presentation and Analysis of data 28 4.1.1 Quantitative data analysis 29 4.1.2 Demographic Questions: 29 4.2 Technical question 32 4.2.1 Quantitative analysis 46 4.3 Quantitative analysis 46 4.4 Chapter summary: 47 4.5 Recommendation and suggestions 48 5 Introduction 49 5.1 Security assurance in cloud computing 50 5.1.1 Confidentiality 51 5.1.2 Correctness Assurance 51 5.1.3 Availability 51 5.1.4 Data Integrity 52 5.2 Security guideline 52 5.2.1 Cloud Service Provider Agent (CSPA) 54 5.2.2 Cloud Data Confidentiality Agent 55 5.2.3 Cloud Data Correctness Agent (CDCorA) 55 5.2.4 Cloud Data Availability Agent (CDAA) 56 5.2.5 Cloud Data Integrity Agent (CDIA) 57 5.3 Testing the framework 58 5.3.1 Strengths: 58 5.3.2 Weakness: 58 5.3.3 Opportunities: 58 5.4 Summery 59 6 Introduction 60 6.1 Research limitation 60 6.2 Recommendation for...
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...Research Report BX2174: Research & Decision-Making for Business KARAN LAKHANPAL Student Number: 12908897 QIN YANG Student Number: 12653971 Lecturer: Dr Andre Kuah Contents 1. EXECUTIVE SUMMARY ............................................................................................ 2 2. BACKGROUND TO THE BUSINESS DECISION OR PROBLEM .............................................. 3 3. THE SPECIFIC RESEARCH OBJECTIVES TO BE ADDRESSED .............................................. 5 4. CRITICAL ANALYSIS OF THE METHODS ...................................................................... 6 5. RESULTS AND DISCUSSION ....................................................................................... 8 6. IMPLICATIONS FOR THE BUSINESS DECISION OR PROBLEM ........................................... 10 7. ETHICAL ISSUES .................................................................................................... 11 8. REFERENCES......................................................................................................... 13 9. APPENDIX ............................................................................................................ 14 The Learning Business Research Report | Campus: 10. 1 CHECKLIST OF ASSESSMENT CRITERIA .................................................................. 18 The Learning Business Research Report 1. Executive Summary This report discusses...
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...Assignment 1: The New Frontier: Data Analytics xxxxxx Professor xxxx CIS500: Information System Decision Making April 17, 20xx Strayer University The New Frontier: Data Analytics Abstract The word “tweet” was first defined as “a chirping noise” whose origin dated back to 1768. Since 2011, Merriam Webster dictionary extends that definition to mean “a post made on the Twitter online messaging service”. Mention of the adoption of “tweet” into the Merriam Webster Dictionary is designed to illustrate two main points; that information can be ambiguous and that technology can reweave the very fabric of human culture. According to research done by Zikopoulos, Eaton, Deroos, Deutsch, & Lapis (2012), there exists 800,000 petabytes (PB) of data stored in the world in the year 2000. By their estimates, that number could reach 35 zettabytes (ZB) by the year 2020 (p. 39). The ability to analyze and process the enormous amount of data is a costly undertaking for companies that are behind the curve and a lucrative business for those that are ahead of the game. Each tweet and post contribution from the users that share the web space further buries the proverbial haystack. It is the ability to sift through the data that determines whether a company can gain traction in their respective industry or if they are simply spinning their wheels. This research paper, centered on Capital Cube and their parent company analytixinsight, will aim to discuss how data analytics is paramount to present...
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...Assignment B Zoe Suet Yee Wan, Jason Lau, Yaoyu Su OutThere Pty Ltd Z3416862 Zoe Suet Yee Wan Z3416733 Jason Lau Z3353653 Yaoyu Su Tutorial: Friday 9-11am Tutor: Jason Simpson Date Submitted: 12/10/2012 Word Count (excluding appendix and overview) : 3113 Word Count for Overview: 301 1 Assignment B Zoe Suet Yee Wan, Jason Lau, Yaoyu Su Executive Summary This following report focuses on providing recommendations and appropriate suggestions to OutThere Pty Ltd regarding the success for FillUp and QuickStop brands. Although FillUp has been growing steadily for the past five years, constant evaluation of efficiency and productivity is necessary for its continual success. On the other hand, standalone QuickStop shops has not been as successful as compared to FillUp as most newly established stores are located in remote areas which may be a contributing factor as to why new tools and strategies are essential to sustain profitability in the long run. Regarding the Chief Information Officer’s priorities to improve purchasing power and distribution efficiency, store layout and product mix, detect fraudulent activities and offer personalised promotion, different types of Business Intelligence Tools will be recommended specifically with the aim to achieve these priorities. 2 Assignment B Zoe Suet Yee Wan, Jason Lau, Yaoyu Su Table of Contents Executive Summary ...........................................................................................
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