feb-15-2021
9 page work
need all seperate folderes.
APA format
Scholarly articles mandatory
9 page work
need all seperate folderes.
APA format
Scholarly articles mandatory
feb-15-2021
Attachment 1
course-835/week-7/Assignments_beeline.html
Bright Dark Blues Grays NightThis week, you have 2 assignments to complete.
Powered by Beeline Readercourse-835/week-7/course-835-discussion-page-1.txt
The article on IRB this week discusses broad consent under the revised Common Rule. When you are doing any sort of research you are going to need to have your research plan approved by the University’s institutional review board or IRB. If you have never heard of this term before, please take a look online and find a brief summary of what it is about, before you read the article. Please answer the following questions in your main post: What are the main issues that the article addresses? What is the Common Rule? How is this issue related to information systems and digital privacy? Please make your initial post and two response posts substantive. A substantive post will do at least two of the following: Ask an interesting, thoughtful question pertaining to the topic Answer a question (in detail) posted by another student or the instructor Provide extensive additional information on the topic Explain, define, or analyze the topic in detail Share an applicable personal experience Provide an outside source (for example, an article from the UC Library) that applies to the topic, along with additional information about the topic or the source (please cite properly in APA) Make an argument concerning the topic. At least one scholarly source should be used in the initial discussion thread. Be sure to use information from your readings and other sources from the UC Library. Use proper citations and references in your post. ============ Write 250 words. Use Scholarly articles and APA 7 format. Mandatory to site the given 2 articles
course-835/week-7/course-835-final-project-pages-7.txt
Risk management is one of the most important components in empowering an organization to achieve its ultimate vision. With proper risk management culture and knowledge, team members will be “speaking” the same language, and they will leverage common analytical abilities to identify and mitigate potential risks as well as exploit opportunities in a timely fashion. In order to consolidate efforts, the existence of an integrated framework is crucial. This is why an ERM is necessary to the fulfillment of any organization's goals and objectives. In your final research project for the course, your task is to write a 7-10 page paper discussing the following concepts: Introduction - What is an ERM? Why Should an Organization Implement an ERM Application? What are some Key Challenges and Solutions to Implementing an ERM? What is Important for an Effective ERM? Discuss at least one real organization that has been effective with implementing an ERM framework/application. Conclusion – Final thoughts/future research/recommendation The paper needs to be approximately 7-10 pages long, including both a title page and a references page (for a total of 9-12 pages). Be sure to use proper APA formatting and citations to avoid plagiarism. Your paper should meet the following requirements: Be approximately seven to ten pages in length, not including the required cover page and reference page. Follow APA7 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion. Support your answers with the readings from the course, the course textbook, and at least FIVE scholarly journal articles to support your positions, claims, and observations, in addition to your textbook. The UC Library is a great place to find supplemental resources. Be clearly and well-written, concise, and logical, using excellent grammar and style techniques. You are being graded in part on the quality of your writing.
course-835/week-7/Enterprise-Risk-Management-course.docx
Course Information
A01 Enterprise Risk Management Spring
Course Format: Online
Course Description
This course goes beyond looking at risk management from the confines of quantitative topics to cover the full spectrum of risks that may emerge in enterprises. It covers a more holistic approach that includes the decisions and actions of employees in an active enterprise. It uses case studies to demonstrate the issues and challenges in total risk management. Finally, the course explore techniques for balancing enterprise risk and reward to enable performance optimization.
Course Objectives
Upon completion of this course:
Students will be able to provide a rigorous business case for both business and mitigation risk-reward decision-making
Students will be able to design and implement an appropriate ERM framework and risk governance structure customized to any type of organization.
Students will be able to quantify all types of risks, including strategic, operational, financial, and insurance
Students will be able to conduct qualitative risk assessments to identify/prioritize key risks from among all risk sources.
Students will be able to develop a clear definition of risk appetite (the aggregate enterprise-level risk limit).
Students will be able to assure the board of directors that key risks are well understood and managed
Students will be able to understand and satisfy ERM requirements from rating agencies, regulators, and shareholders
Learner Outcomes
Learn how to perform research identifying and analyzing technological challenges
Build critical thinking skills to develop and apply solutions that achieve strategic and tactical IT-business alignment
Develop professional skills and expertise to advance knowledge in your chosen field or discipline within information technology
Conduct research with professional and ethical integrity
Address complex technical questions and challenge established knowledge and practices in the area
Identify, comprehend, analyze, evaluate and synthesize research
Communicate effectively and employ constructive professional and interpersonal skills
Critically evaluate current research and best practices
Demonstrate IT leadership skills at the team and enterprise levels following tenets of professional, social, and ethical responsibility
Recommend IT strategies that support enterprise mission and objectives
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Course Schedule |
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Weekly Unit |
Readings/Topics |
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Risk Management and Enterprise Risk Management |
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Beasley, M. S. (2016). What is Enterprise Risk Management? Retrieved from https://erm.ncsu.edu/az/erm/i/chan/library/What_is_Enterprise_Risk_Managemen |
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Week 1
Jan 04 - 10, 2021 |
Hopkin, P. (2017). Fundamentals of Risk Management: Understanding, Evaluatin and Implementing Effective Risk Management (Vol. Fourth Edition). New York: Kogan Pages 96-103. ( Part Two, Section 08 Enterprise Risk Management ) http://search.ebscohost.com/login.aspx? direct=true&AuthType=shib&db=e000xna&AN=1446715&site=eds- live&custid=s8501869&groupid=main&profile=eds_new |
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Integrating ERM with Strategy |
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Enterprise Risk Management Integrating with Strategy and Performance Executive |
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Week 2 | |
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Do, H., Railwaywalla, M., & Thayer, J. (2016). Integration of ERM with Strategy (p. |
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Jan 11 |
Retrieved from Poole College of Management, NCSU website: |
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- 17, |
https://erm.ncsu.edu/az/erm/i/chan/library/Integration_of_ERM_and_Strategy_Case |
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2021 |
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Risk Management Frameworks |
Week 3 Discussion Week 3 Research Paper
Due: Sunday night 11:59 PM Eastern |
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Lopes, M., Guarda, T. & Oliveira, P. (2019). How ISO 27001 Can |
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Help Achieve GDPR Compliance. 2019 14th Iberian Conference |
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Week |
on Information Systems and Technologies (CISTI), pp. 1-6. |
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3 |
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Jan 18 - 24, 2021 |
Al-Ahmad, W., & Mohammad, B. (2013). Addressing Information Security Risks by Adopting Standards. International Journal of Information Security Science, 2(2), 28–43. |
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Week 4
Jan 25 - 31, 2021 |
Risk Management Frameworks and Assessment
Mackita, M., Shin, S.-Y., & Choe, T.-Y. (2019). ERMOCTAVE: A Risk Management Framework for IT Systems Which Adopt Cloud Computing. Future Internet, 11(9), 195. Retrieved from https://doi.org/10.3390/fi11090195 Puchley, T., & Toppi, C. (2018). ERM: Evolving from Risk Assessment to Strategic Risk Management. HFM (Healthcare Financial Management), 1–5. |
Week 4 Discussion Week 4 Research Paper
Due: Sunday night 11:59 PM Eastern |
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Risk Management Forensics |
Week 5 Discussion Week 5 Research Paper
Due: Sunday night 11:59 PM Eastern |
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Week 5
Feb 01 - 07, 2021 |
Hou, J., Li, Y., Yu, J. & Shi, W. (2020). A Survey on Digital Forensics in Internet of Things IEEE Internet of Things Journal, I(1),1-15,. Chen, J. & Zhu, Q. (2019). Interdependent Strategic Security Risk Management with Bounded Rationality in the Internet of Things. IEEE Transactions on Information Forensics and Security, 14(11), 2958-2971
Schiller, F., & Prpich, G. (2014). Learning to organise risk management in organisations: what future for enterprise risk management? Journal of Risk Research, 17(8), 999–1017. https://doi.org/10.1080/13669877.2013.841725 |
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Borek, A. (2014). Total Information Risk Management: Maximizing the Value of Data and Information Assets (Vol. First edition). Amsterdam: Morgan Kaufmann |
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Digital Forensics |
Week 6 Discussion Week 6 Research Paper Executive Program Practical Connection Assignment
Due: Sunday night 11:59 PM Eastern |
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Montasari, R., & Hill, R. (2019). Next-Generation Digital Forensics: Challenges and Future Paradigms. 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3), Global Security, Safety and Sustainability (ICGS3), 205. https://doi.org/10.1109/ICGS3.2019.8688020 |
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Week 6
Feb 08 - 14, 2021 |
Sahinoglu, M., Stockton, S., Barclay, R. M., & Morton, S. (2016). Metrics Based Risk Assessment and Management of Digital Forensics. Defense Acquisition Research Journal: A Publication of the Defense Acquisition University, 23(2), 152–177. https://doi.org/10.22594/dau.16-748.23.02 |
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Nnoli, H. Lindskog, D, Zavarsky, P., Aghili, S., & Ruhl, R. (2012). The Governance of Corporate Forensics Using COBIT, NIST and Increased Automated Forensic Approaches, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Conference on Social Computing, Amsterdam, 734-741. |
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IT Governance and IT Risk Management Practices |
Week 7 Discussion ITS835 Final Project
Due: Sunday night 11:59 PM Eastern |
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Vincent, N. E., Higgs, J. L., & Pinsker, R. E. (2017). IT Governance and the Maturity of IT Risk Management Practices. Journal of Information Systems, 31(1), 59–77. https://doi.org/10.2308/isys-51365 |
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Week 7
Feb 15 - 21, 2021 |
Risk Inventory
Etges, A. P. B. da S., Grenon, V., Lu, M., Cardoso, R. B., de Souza, J. S., Kliemann Neto, F. J., & Felix, E. A. (2018). Development of an enterprise risk inventory for healthcare. BMC Health Services Research, 18(1), N.PAG. https://doi.org/10.1186/s12913-018-3400-7 |
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IRB |
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Implementing Regulatory Broad Consent Under the Revised Common Rule: Clarifying Key Points and the Need for Evidence. (2019). Journal of Law, Medicine & Ethics, 47(2), 213–231. https://doi.org/10.1177/1073110519857277 |
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The Future of ERM |
Week 8 Discussion Due: Thursday night 11:59 PM Eastern |
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Schiller, F., & Prpich, G. (2014). Learning to |
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organise risk management in organisations: what |
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future for enterprise risk management? Journal of |
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Risk Research, 17(8), 999– |
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22 - |
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26, |
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2021 |
*SHORT WEEK, |
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Check with instructor for last day of class and |
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last day to submit assignment |
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course-835/week-7/Objectives_beeline.html
Bright Dark Blues Grays NightBy the end of this week, you should be able to:
- Understand IRB
- Learn the importance of IT governance
- Analyze risk inventories
- Describe some IT risk management best practices
course-835/week-7/Reading Assignments_beeline.html
Bright Dark Blues Grays NightIT Governance and IT Risk Management Practices"
Vincent, N. E., Higgs, J. L., & Pinsker, R. E. (2017). IT Governance and the Maturity of IT Risk Management Practices. Journal of Information Systems, 31(1), 59–77. https://doi.org/10.2308/isys-51365
Etges, A. P. B. da S., Grenon, V., Lu, M., Cardoso, R. B., de Souza, J. S., Kliemann Neto, F. J., & Felix, E. A. (2018). Development of an enterprise risk inventory for healthcare. BMC Health Services Research, 18(1), N.PAG. https://doi.org/10.1186/s12913-018-3400-7
Powered by Beeline Readercourse-835/week-7/Reading IRB and Protection of Human Subjects_beeline.html
Bright Dark Blues Grays NightWhile this reading is not part of ERM, it does deal with risk - and the risk to human subjects. When you work on your dissertation, you will have to get approval from UC's IRB board, so it's helpful as you work through your class to get familiar with what you will need when beginning to work on your dissertation.
Implementing Regulatory Broad Consent Under the Revised Common Rule: Clarifying Key Points and the Need for Evidence. (2019). Journal of Law, Medicine & Ethics, 47(2), 213–231. https://doi.org/10.1177/1073110519857277
There is a lot more information at the Graduate School, so get acquainted with all the resources available: https://www.ucumberlands.edu/gradschool
Make sure to click the link Explore the Dissertation Process for much more information: https://www.ucumberlands.edu/gradschool/dissertation
Powered by Beeline Readercourse-835/week-7/Reminder_ Plagiarism_beeline.html
Bright Dark Blues Grays NightStudents - Plagiarism will continuously be checked even after the term is over. Should plagiarism be confirmed, one of the consequences will apply. This could potentially change your final grade. YES - your grade can be changed after the term is over!
As a reminder, before completing the final research paper, please review UC's academic honesty policy again:
Academic Integrity/ Plagiarism (This information is included in all syllabi at University of the Cumberlands)
At a Christian liberal arts university committed to the pursuit of truth and understanding, any act of academic dishonesty is especially distressing and cannot be tolerated. In general, academic dishonesty involves the abuse and misuse of information or people to gain an undeserved academic advantage or evaluation. The common forms of academic dishonesty include:
- Cheating – using deception in the taking of tests or the preparation of written work, using unauthorized materials, copying another person’s work with or without consent, or assisting another in such activities.
- Lying – falsifying, fabricating, or forging information in either written, spoken, or video presentations.
- Plagiarism—using the published writings, data, interpretations, or ideas of another without proper documentation.
Plagiarism includes copying and pasting material from the Internet into assignments without properly citing the source of the material. Plagiarism can happen by mistake - so you need to be careful!
Episodes of academic dishonesty are reported to the Vice President for Academic Affairs. The potential penalty for academic dishonesty includes a failing grade on a particular assignment, a failing grade for the entire course, or charges against the student with the appropriate disciplinary body.
Plagiarism Offense // Consequence:
- First Offense // 0 on the assignment
- Second Offense // Removal from course = F for course grade
- Third Offense // Dismissal from University
Remember - Self-plagiarism is still plagiarism and not allowed – you cannot use previous class work from this or any previous courses whether or not it was originally submitted to University of the Cumberlands. SafeAssign is an international global database - meaning it checks for plagiarism across the globe.
Powered by Beeline Readercourse-835/week-7/Week 7 Overview_beeline.html
Bright Dark Blues Grays NightIn week 7, students will examine maturity models in IT governance and look at some risk management best practices. Additionally, students will examine ways on how to analyze and understand risk inventories. Lastly, students will examine risk from a human perspective focus and how rules govern the protection of human when participating in research.
Powered by Beeline Readercourse-835/week-7/Week 7 Preview_beeline.html
Bright Dark Blues Grays NightWelcome to Week 7, CLASS!
I trust, we had a good week!
Please review the following article to know more about Enterprise Risk Management.
https://www.pmi.org/learning/library/enterprise-risk-management-application-implementation-5831
The activities this week will help us to know about the role of IRB in the dissertation, which include:
When conducting research, we must sometimes gain permission from the governing body. In research, we call this the Institutional Review Board (IRB).
For Discussion, please ensure we respond to the prompts in our own words. If we need to quote or use content from a source, ensure that we paraphrase the information to demonstrate our knowledge of the content. Remember to ENGAGE the other students in the class in dialogue, as they are discussion thread.
• Providing the ITS835 Final Project Assignment: Be sure to answer each question and site your work to support your position.
Powered by Beeline Readercourse-836/week-7/Assignments_beeline.html
Bright Dark Blues Grays NightThis week you have 1 assignment to complete.
Powered by Beeline Readercourse-836/week-7/course-836-discussion-page-1.txt
There is much discussion regarding Data Analytics and Data Mining. Sometimes these terms are used synonymously but there is a difference. What is the difference between Data Analytics vs Data Mining? Please provide an example of how each is used. ===== Write 250 words. Use Scholarly articles and APA 7 format. Mandatory to site the given articles in Reading Assignments_beeline
course-836/week-7/Formalizing Your Information Technology Dissertation_beeline.html
Bright Dark Blues Grays NightDr. Steven Brown, PhD IT Program Director, has created a presentation on how to move on from the topic to examine the next stage in a doctoral dissertation, and where research questions will flow from.
https://us-lti.bbcollab.com/recording/ec59190a6535499ca19e8466dc7d2aa5
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course-836/week-7/Objectives_beeline.html
Bright Dark Blues Grays NightBy the end of this week, you should be able to:
- Describe data mining
- Identify types of data mining tools
- Explain how data mining and analytics work together
course-836/week-7/Reading Assignments_beeline.html
Bright Dark Blues Grays NightBuntine, W. (2020). Machine learning after the deep learning revolution. Frontiers of Computer Science, 14(6), 1.
Cunha, M. N., Chuchu, T., & Maziriri, E. T. (2020). Threats, Challenges, and Opportunities for Open Universities and Massive Online Open Courses in the Digital Revolution. International Journal of Emerging Technologies in Learning, 15(12), 191–204. https://doi.org/10.3991/ijet.v15i12.13435
Marcu, D., & Danubianu, M. (2019). Learning Analytics or Educational Data Mining? This is the Question.. BRAIN: Broad Research in Artificial Intelligence & Neuroscience, 10, 1–14.
Poudyal, S., Akhtar, Z., Dasgupta, D., & Gupta, K. D. (2019). Malware Analytics: Review of Data Mining, Machine Learning and Big Data Perspectives. 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Computational Intelligence (SSCI), 2019 IEEE Symposium Series On, 649–656. https://doi.org/10.1109/SSCI44817.2019.9002996
Powered by Beeline Readercourse-836/week-7/Week 7 Overview - Big Data Mining vs Analytics_beeline.html
Bright Dark Blues Grays Night
Data mining is the exploration and analysis of large data to discover meaningful patterns and rules. Data mining aims to predict future outcomes. Additionally, data mining techniques are used to build machine learning (ML) models that power modern artificial intelligence (AI) applications such as search engine algorithms and recommendation systems.
Benefits of Data Mining:
- Automated Decision-Making
- Accurate Prediction and Forecasting
- Cost Reduction
- Customer Insights
While a powerful process, data mining is hindered by the increasing quantity and complexity of big data. Where exabytes of data are collected by firms every day, decision-makers need ways to extract, analyze, and gain insight from their abundant repository of data.
Data mining has two primary processes: supervised and unsupervised learning. Data mining software is a tool to convert raw and unstructured data into useful information in order to optimize the decision making ability. This software offers enterprises an ability of predictive analysis which helps them forecasting marketing strategy and consumers behavior. Steps involved in data mining include data collection, data processing and then software sort the data depending on user’s result in the form of graph or table.
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