solved Assessment 5 Instructions: Data MiningPerform data mining activities on two

Assessment 5 Instructions: Data MiningPerform data mining activities on two Excel datasets. Prepare a 4-5 page report of findings, including whether datasets accurately depict performance, the use of data sampling methods in strategic decision making, and conclusions and recommendations about improving patient service and staff performance. Include in the report the analysis of the raw data in Excel data analysis tables.IntroductionData mining is a statistical analysis process used to extract data to provide useful information. Beginning with raw data, a data analyst organizes the data, rearranges it, and then searches for patterns. After identifying the patterns, the analyst can turn the data into usable information. In this assessment you will perform data mining activities and apply the results to different uses in health care information settings.Demonstration of ProficiencyBy successfully completing this assessment, you will demonstrate your proficiency in the course competencies through the following assessment scoring guide criteria: Competency 3: Use data analysis skills to support health information integrity and data quality.Analyze data samples.Explain how to use data sampling methods and data mining to inform strategic decision making.Recommend quality of care improvements based on statistical analysis.Competency 4: Apply statistical strategies to analyze health care data.Organize raw data.Perform data mining activities.Competency 5: Communicate in a professional manner to support health care data analytics.Create a clear, well-organized, professional document that is generally free of errors in grammar, spelling, and punctuation.Follow APA style and formatting guides for citations and references. PreparationYou will be working with datasets in Excel spreadsheets for this assessment. Download and review these datasets now: Dataset 1 Clinic Performance 2017 [XLSX].Dataset 2 Nursing Performance 2016 [XLSX].InstructionsAs a Vila Health data analyst, you have been asked to work on a project related to customer satisfaction and nursing staff performance. You will analyze two datasets. One concerns clinic performance, specifically, patient wait times and office visit lengths. The other dataset is on nursing performance. After analyzing these two datasets, you will compose a report for the clinic’s physicians based on your analysis.Dataset 1: Clinic PerformanceThis first dataset contains raw data about clinic performance from a customer-service perspective. First, organize and analyze the raw data in Excel data analysis tables. You will include these tables in your report. Write your report about Dataset 1. Be sure to include these headings and address the bullets following each heading:Accurate Depiction of Clinic Performance.Explain whether the sample can accurately depict clinic performance, noting variations and patterns.Data Sampling Methods and Strategic Decision Making.Describe how to use data sampling methods in strategic decision making.Conclusions and Recommendations About Clinic Physicians and Customer Service.Draw conclusions about clinic physicians and customer service.Make two recommendations for improving patient service based on your analysis.Dataset 2: Nursing Staff PerformanceDataset 2 provides information on nursing staff performance on two tasks. The data show a decrease in nursing staff productivity at one Vila Health clinic in the past few months. Use the Nursing Data Worksheet and the Pivot Table Report, both contained in Dataset 2 Nursing Performance 2016, to perform data mining techniques to determine how nursing staff performed when completing Tasks 1 and 2. Organize and analyze the raw data in Excel data analysis tables. You will include these tables in your report.Note: Be careful of filters. Be sure to check data from various years.Write your report, including all of the following:Data Mining Techniques to Evaluate Nursing Staff Performance on Tasks.Explain how each of these data mining techniques can be used to evaluate nursing staff task performance:Genetic algorithms.Neural networks.Predictive modeling.Rule induction.Decision trees.K-Nearest neighbor.Include examples of the use of each data mining technique in relation to the nursing data.Data Mining and Strategic Decision Making.Describe the use of data mining in strategic decision making.Conclusions and Recommendations About Nursing Staff Performance.Draw conclusions about nursing performance on tasks.Create two recommendations for improving nursing performance.ConclusionSummarize the findings of your analysis of the two datasets. Draw conclusions about how the information from the datasets might be connected. For example, how might physician performance impact nursing tasks? Or what is the association between customer satisfaction and nursing task performance? Additional RequirementsFormat: Word document, including data analysis tables from Excel.Length: Four to five double-spaced pages.Font: Times New Roman, 12 point.References and citations: Include citations and references in APA format and style.Writing: Create a clear, well-organized, professional document that is generally free of errors in grammar, punctuation, and spelling. Resources: Data CollectionCapella University Health Care Administration Undergraduate Library Research Guide.Please consult this guide as needed to conduct independent research on course topics. This resource will direct you to scholarly, peer-reviewed, and authoritative resources.Horton, L. A. (2017). Calculating and reporting healthcare statistics (5th Rev. ed.). Chicago, IL: AHIMA Press. Available from the bookstore. Chapter 12, “Basic Research Principles,” pages 283–307.Chapter 13, “Inferential Statistics in Healthcare,” pages 309–320.Cleary, M., Horsfall, J., & Hayter, M. (2014). Data collection and sampling in qualitative research: Does size matter? Journal of Advanced Nursing, 70(3), 473–475.Kandola, D., Banner, D., O’Keefe-McCarthy, S., & Jassal, D. (2014). Sampling methods in cardiovascular nursing research: An overview. Canadian Journal of Cardiovascular Nursing, 24(3), 15–18.Roberts, P. (2015). Data sampling for the right reasons. Business Intelligence Journal, 20(1), 33–38.Resources: Data MiningHorton, L. A. (2017). Calculating and reporting healthcare statistics (5th Rev. ed.). Chicago, IL: AHIMA Press. Available from the bookstore. Chapter 14, “Data Analytics,” pages 321–330.Lismont, J., Janssens, A., Odnoletkova, I., Vanden Broucke, S., Caron, F., & Vanthienen, J. (2016). A guide for the application of analytics on healthcare processes: A dynamic view on patient pathways. Computers in Biology and Medicine, 77, 125–134.Ullah, Z., Fayaz, M., & Iqbal, A. (2016). Critical analysis of data mining techniques on medical data. International Journal of Modern Education and Computer Science, 8(2), 42–48.Data Mining | Transcript.In under five minutes, this Capella media piece presents some basic assumptions about leading a data mining project. Resources: Data PresentationHorton, L. A. (2017). Calculating and reporting healthcare statistics (5th Rev. ed.). Chicago, IL: AHIMA Press. Available from the bookstore. Chapter 11, “Presentation of Data,” pages 245–281.Ghazisaeidi, M., Safdari, R., Torabi, M., Mirzaee, M., Farzi, J., & Goodini, A. (2015). Development of performance dashboards in healthcare sector: Key practical issues. Acta Informatica Medica, 23(5), 317–321.Presentation of Data | Transcript.This short (just under three minutes) Capella media piece will help you choose the best option (bar chart, histogram, pie chart, et cetera) to present your data.