Prior to beginning work on this assignment,
Read Chapter 9 (pp. 276–281) of Systems Analysis and Design.
Watch HIA612 Formulating a Database (Links to an external site.)
Put yourself in the position of a clinician coming to an information technology session. You are asked to present information about the basics of relational database design. Specifically, you are asked to communicate the similarities and differences between the terms entity, table, field, and record as they relate to DBMS systems.
Your audience is clinicians who do not necessarily understand these four fundamental database concepts. Using examples from the health field, create a PowerPoint presentation explaining each of these four concepts for a non-technical audience. Also, explain how these detailed concepts relate to the value of a business intelligence applications such as a data warehouse in a health care setting.
Detailed speaker notes should be used to explain each textual and graphic slide in detail.
In your presentation,
Articulate the meaning of the term entity relating examples to the types of entities that we might see in a health-related database.
Discuss the difference between a field and a record in a relational database.
Articulate the purpose of data tables in relational database design.
Compose examples of how these concepts might relate to developing a comprehensive data warehouse in a health setting.
The Teaching Database Basics presentation
Must be six to eight slides in length (not including title and references slides) and formatted according to APA Style (Links to an external site.) as outlined in the Writing Center’s How to Make a PowerPoint Presentation (Links to an external site.)
Must include a separate title slide with the following:
Title of presentation
Course name and number
Must use at least two scholarly and/or peer-reviewed sources in addition to the course text.
The Scholarly, Peer-Reviewed, and Other Credible Sources (Links to an external site.) table offers additional guidance on appropriate source types. If you have questions about whether a specific source is appropriate for this assignment, please contact your instructor. Your instructor has the final say about the appropriateness of a specific source for a particular assignment.
To assist you in completing the research required for this assignment, view this Library Quick ‘n’ Dirty (Links to an external site.) tutorial, which introduces the University of Arizona Global Campus Library and the research process, and provides some library search tips.
Must document any information used from sources in APA Style as outlined in the Writing Center’s APA: Citing Within Your Paper (Links to an external site.)
Must include a separate reference slide that is formatted according to APA Style as outlined in the Writing Center. See the APA: Formatting Your References List (Links to an external site.) resource in the Writing Center for specifications.Define data mining as an enabling technology for business analytics and understand the objectives and benefits of data mining.
Become familiar with the wide range of applications of data mining Learn the standardized data mining processes Learn different methods and algorithms of data mining
Build awareness of the existing data mining software tools Understand the privacy issues, pitfalls, and myths of data mining
Describe text analytics and understand the need for text mining
Differentiate among text analytics, text mining, and data mining and the application areas for text mining
Know the process of carrying out a text mining project
Describe sentiment analysis
Develop familiarity with popular applications of sentiment analysis Learn the common methods for sentiment analysis Become familiar with speech analytics as it relates to sentiment analysis
Understand the applications of prescriptive analytics techniques
Understand the basic concepts of analytical decision modeling and understand the concepts of analytical models for selected decision problems
Describe how spreadsheets can be used for analytical modeling and solutions Explain the basic concepts of optimization and when to use them
Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking Understand the concepts and applications of different types of simulation and understand potential applications of discrete event simulation
The Portfolio Project, due at the end of Week 8, is a statistical analysis using one or more of the statistical analysis approaches presented in Modules 4 and 5. These various approaches are designed to produce business intelligence to resolve problems and enable management to make informed business decisions.
You will be conducting these analyses based on your evaluation and preparation of the data in Module 6 using the IBM SPSS statistical analysis software. This week’s lecture contains the information you need for downloading and using the SPSS software. Having completed your analysis for the Week 6 Critical Thinking Assignment, you will refine and present your findings in the Portfolio Project and associate them to business knowledge that can be used to enhance the decision-making abilities of the organization.
Your Portfolio Project submission should include the following sections:
Introduction: Introduce the organization, the business problem that you defined in the Week 6 Critical Thinking Assignment, and the data that was presented to you.
Data Preparation: Discuss the data and any quality and accuracy concerns that you discovered. Describe your approach to processing the data to improve the chances of discovering new knowledge from the existing data.
Analysis Approach: Describe your chosen analysis approach. Include your assessment of how effective the method was in discovering new relevant knowledge within the data.
Findings: Present and discuss your findings as the result of your analysis. Include any supporting data, graphics, tables, charts, etc., along with your analysis of the findings. Discuss any correlations, associations or patterns that you identified in the data.
Application: Briefly discuss your findings as they pertain to the business problem. How might the organization use this new knowledge to make informed business decisions to resolve the business problem?
Discussion: Discuss the limitations of your data mining process in discovering useful business intelligence. Additionally, discuss other research or analysis methods. What methods might another data mining expert consider using should the organization need further analysis on this data and business problem?
This paper revisits the seminal work by (Heer & Shneiderman, 2012) on interactive dynamics for visual analysis. The paper reflects on the impact and evolution of the original taxonomy of interactive dynamics, which covers seven categories: data and view specification, view manipulation, process and provenance, analysis guidance, visual presentation design, data presentation design, and social data analysis. The paper also discusses the current challenges and opportunities for advancing interactive dynamics research.
Sacha D., Stoffel A., Stoffel F., Kwon B.C., Ellis G., & Keim D.A. (2019). Knowledge Generation Model for Visual Analytics. IEEE. Retrieved from
(Sacha et al, 2019) The paper presents a study on the use of multi-touch technology for interactive data visualization. The study compared the effectiveness of traditional mouse- based interaction with multi-touch interaction on a large display for visualizing complex data sets. The results showed that multi-touch interaction was more efficient,accurate, and enjoyable. The authors suggest that multi-touch technology has significant benefits for enhancing collaboration and communication in data exploration and decision-making processes.
Liu Z., Jiang B., Heer J., & Wang J. (2019). imMens: Real-time Visual Querying of Big Data. Computer Graphics Forum, 38(3), 421-431. Retrieved from
This paper (Liu et al, 2019) presents imMens, a system for real-time visual querying of big data. imMens uses a novel data processing and rendering pipeline that leverages the GPU to support interactive exploration of billions of records. imMens also provides a web-based interface that allows users to create and manipulate multiple coordinated views of the data.
Vartak M., Rahman S., Madden S., Parameswaran A., & Polyzotis N. (2017). SEEDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics. Proceedings of the VLDB Endowment, 10(6), 625-636. Retrieved from
This paper (Vartak et al, 2017) introduces SEEDB, a system for efficient data-driven visualization recommendations to support visual analytics. SEEDB automatically generates visualizations that highlight interesting or anomalous patterns in the data, based on a deviation-based scoring metric. SEEDB also employs several optimization techniques to speed up the computation of the scores and the generation of the visualizations.
Hullman J., & Diakopoulos N. (2011). Visualization Rhetoric: Framing Effects in Narrative Visualization. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2231-2240. Retrieved from
(Hullman et al, 2011) This paper explores the concept of visualization rhetoric, which is the use of visual techniques to influence the interpretation and perception of data stories. The paper identifies four types of framing effects in narrative visualization: message framing, visual framing, interactivity framing, and context framing. The paper also discusses the implications and challenges of visualization rhetoric for design and evaluation.
Kosara R., & Mackinlay J. (2013). Storytelling: The Next Step for Visualization. Computer, 46(5), 44-50. Retrieved from
(Kosara et al, 2013) This paper argues that storytelling is the next step for visualization, as it can help communicate complex data and insights more effectively and engagingly to a broader audience. The paper defines storytelling as the use of narrative elements such as characters, plot, and structure to convey a message or a point of view. The paper also reviews some examples and principles of storytelling in visualization.
Vc ., & Gotz D. (2014). Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics. IEEE Transactions on Visualization and Computer Graphics, 20(12), 1653-1662. Retrieved from
(Vc et al, 2014) This paper proposes a progressive visual analytics approach that enables user-driven visual exploration of in-progress analytics results. The approach allows users to interact with partial results as they are computed, and to steer the analysis process by providing feedback or modifying parameters. The paper also presents a system prototype that implements the approach for clustering analysis.
Viegas F.B., Wattenberg M., Van Ham F., Kriss J., & McKeon M. (2007). ManyEyes: A Site for Visualization at Internet Scale. IEEE Transactions on Visualization and Computer Graphics, 13(6), 1121-1128. Retrieved from
(Viegas et al, 2007) This paper presents ManyEyes, a web site that enables users to create and share interactive visualizations of their own data sets at internet scale. ManyEyes provides a variety of visualization types, such as charts, maps, networks, and text analysis tools, that can be applied to any tabular or textual data uploaded by users. ManyEyes also supports social data analysis by allowing users to comment on and discuss each other’s visualizations.
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