Please go through the document completely before providing the answer.
Need a paper of 2-3 pages with references on the topic mentioned in the document.
Need a presentation of 2-3 slides with references on the same topic mentioned in the document.
No Plagiarism.
Essay: Minimum 600 words
The false discovery rate (FDR) provides an error metric to measure the rate of false positives/false discoveries. This is pertinent data as we process the data set. Is there a point when you would not correct this?
In addition, please discuss the relationship between Type I and II errors. Is this relevant to the analysis?
Discussion : Minimum 300 words
In the academic workplace, there has been the push for publications in peer reviewed journals. This and the research itself heightens the University’s exposure to other universities, potential students, and others. This culture may create a systemic bias against the null hypothesis and the subsequent researchers may find themselves in a circumstance where there is a difficulty with showing the hypotheses are false.
Would the proportion of these instances increase with a decrease in sample size?
Submit Assignment
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What to hand in?
What needs to be done?
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Getting Started
import math
import unittest
def findWaterContainerPath(a, b, c):
""" DOCUMENTATION FOR THIS FUNCTION """
starting_state = (0, 0)
final_path = list()
final_path.append(starting_state)
""" THIS IS WHERE THE REAL WORK FOR THIS ASSIGNMENT WILL BE """
return final_path
""" ADD ANY OTHER (HELPER) FUNCTIONS THAT ARE NEEDED HERE """
class TestWaterContainerGraphSearch(unittest.TestCase):
def testFindWaterContainerPath(self):
""" INSERT DESCRIPTION OF WHAT THIS TEST IS CHECKING """
""" IMPLEMENT YOUR FIRST TEST HERE """
pass
""" ADD MORE TESTS TO CHECK YOUR FUNCTIONS WORKS FOR OTHER VALUES """
def main():
capacity_a = input("Enter the capacity of container A: ")
capacity_b = input("Enter the capacity of container B: ")
goal_amount = input("Enter the goal quantity: ")
# ADD SOME TYPE/VALUE CHECKING FOR THE INPUTS (OR INSIDE YOUR FUNCTION)
if int(goal_amount) % math.gcd(int(capacity_a), int(capacity_b)) == 0:
path = findWaterContainerPath(int(capacity_a), int(capacity_b), int(goal_amount))
else:
print("No solution for containers with these sizes and with this final goal amount")
print(path)
# unittest_main() - run all of TestWaterContainerGraphSearch's methods (i.e. test cases)
def unittest_main():
unittest.main()
# evaluates to true if run as standalone program
if __name__ == '__main__':
main()
unittest_main()Good luck!
Assignment 5 RubricAssignment 5 RubricCriteriaRatingsPtsThis criterion is linked to a Learning OutcomeProgram Execution and Output30.0 to >25.0 ptsGoodProgram executes with no syntax or runtime errors and produces nearly all of the appropriate output when run through the test script (26pts – 30pts).25.0 to >10.0 ptsFairProgram executes with via the test script but does not produce all of the desired/correct output (11pts – 25pts).10.0 to >0 ptsPoorProgram does not execute via the test script without some modification and/or produces none or very little of the desired output in the test script (0pts – 10 pts).30.0 pts
This criterion is linked to a Learning OutcomeCode Logic and Design40.0 to >30.0 ptsGoodProgram has all required classes, variables, and methods defined (31pts – 40pts).30.0 to >10.0 ptsFairProgram has at least some but not all requisite classes, methods, or variables (11pts – 30pts).10.0 to >0 ptsPoorProgram has few or none of the classes, variables, or methods that were required (0pts – 10pts).40.0 pts
This criterion is linked to a Learning OutcomeCoding Standards and Documentation30.0 to >20.0 ptsGoodProgram has documentation for each class, method, function and this is displayed when using Python’s help function, “help()”. Documentation should clearly explain how and why methods are implemented as is (e.g. when and how does HashTable choose to resize itself). In addition to this, for full points the code should be consistently formatted, with consistency in how methods and variables are named (21pts – 30pts).20.0 to >10.0 ptsFairProgram has some documentation and is somewhat consistent in formatting, variable/method naming, etc. However, at least one of those is missing/incorrect (11pts – 20pts).10.0 to >0 ptsPoorProgram has little to no documentation, is not structured/formatted well, and does follow a consistent approach towards naming variables/method (0pts – 5pts).30.0 pts
This criterion is linked to a Learning OutcomeTesting10.0 to >5.0 ptsGoodProgram tests edge cases, or scenarios that would produce unexpected results using Python’s unittest (4pts – 5pts).5.0 to >2.0 ptsFairProgram tests for some edge cases but is missing important/obvious edge cases and may not be using unittest (3pts – 4pts).2.0 to >0 ptsPoorProgram has very few or no checks for edge cases, and does not make use of unittest (0pts – 2pts).10.0 pts
Total Points: 110.0PreviousNext
Please submit a draft of your final project for review. Final submissions are due during week 15 (from the week 15 content folder)
Final Project Prompt:
The final portfolio project is a three- part activity. You will respond to three separate prompts but prepare your paper as one research paper. Be sure to include at least one UC library source per prompt, in addition to your textbook (which means you’ll have at least 4 sources cited).
Start your paper with an introductory paragraph.
Prompt 1 “Data Warehouse Architecture” (2-3 pages): Explain the major components of a data warehouse architecture, including the various forms of data transformations needed to prepare data for a data warehouse. Also, describe in your own words current key trends in data warehousing.
Prompt 2 “Big Data” (1-2 pages): Describe your understanding of big data and give an example of how you’ve seen big data used either personally or professionally. In your view, what demands is big data placing on organizations and data management technology?
Prompt 3 “Green Computing” (1-2 pages): One of our topics in Chapter 13 surrounds IT Green Computing. The need for green computing is becoming more obvious considering the amount of power needed to drive our computers, servers, routers, switches, and data centers. Discuss ways in which organizations can make their data centers “green”. In your discussion, find an example of an organization that has already implemented IT green computing strategies successfully. Discuss that organization and share your link. You can find examples in the UC Library.
Conclude your paper with a detailed conclusion section.
The paper needs to be approximately 5-8 pages long, including both a title page and a references page (for a total of 7-10 pages). Be sure to use proper APA formatting and citations to avoid plagiarism.
Your paper should meet the following requirements:
• Be approximately 5-8 pages in length, not including the required cover page and reference page.
• Follow APA6 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 three scholarly journal articles from the UC library to support your positions, claims, and observations, in addition to your textbook. The UC Library is a great place to find 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.
Over the past several years, the Chief Executive Officer (CEO) of your company has read articles on Internet Control Message Protocol (ICMP) attacks and the use of packet sniffers to aid in hacking into computer networks. Though the CEO understands that this is a serious concern, he does not know what, if anything, is being done to protect the organization’s network against such attacks.
1. In your own words explain why security architecture is important to an organization? and what does an assessor need to understand before she or he can perform an assessment?
2. story or article related to Information Security/Information Technology. write a summary of what you learned, please also provide a link to the original article.
Please read the two discussion post and provide response to each discussion post in 75 to 100 words
Post1:
Quantitative Risk Analysis uses available relevant and verifiable data to produce a numerical value which is then used to predict the probability (and hence, acceptability) of a risk event outcome. Qualitative Risk Analysis, on the other hand, applies a subjective assessment of risk occurrence likelihood (probability) against the potential severity of the risk outcomes (impact) to determine the overall severity of a risk. (Shuttleworth, Mike, 2017)
Qualitative risk assessment excels at giving the risk assessor and the risk manager information about how well the control is currently implemented.
In qualitative risk analysis, impacts and likelihood evaluated using some established methods. After evaluation, we describe them in terms such as very high, high, moderate, low, very low.
Identity (or mark) risks for further analysis.
The risk which is not marked for further analysis, it identifies actions for them based on the combined effects of the probability of occurrence and impact on project objectives.
Qualitative analysis does not analyze the risks mathematically to identify the probability and likelihood. Instead, it uses stakeholders inputs to judge the impact.
Quantitative Risk Analysis uses the probability distributions to characterize the risk’s probability and impact.
The risk assessment methodology you use should depend on what you are trying to measure and what outcomes you’d like to see from that measurement. A quantitative risk assessment focuses on measurable and often pre-defined data, whereas a qualitative risk assessment is based more so on subjectivity and the knowledge of the assessor. A quantitative risk management methodology is best suited for a detailed look at comparing like-things across your organization, while a quantitative risk assessment is best for evaluating the implementation of a framework that does not inherently have pre-defined values. ( Buzz Hillestad)
Post2:
A common question that companies ask during the risk management process is whether a quantitative or qualitative approach should be taken. The good news is that you can actually make your method more effective and achieve the desired level of security by using both approaches. On the other hand , quantitative risk analysis is objective. It uses comprehensible data to evaluate the impacts of risk on overruns, differences in reach, use of resources and delay schedules. In the end, the objective is the same; the difference is that a more analytical, data-intensive approach is needed.
“In layman’s terms, quantitative risk analysis assigns a numerical value to extant risks- risk A has a 40% chance of occurring, based on quantifiable data (fluctuations in resource costs, average activity completion time, logistics etc.) and a 15% chance of causing a delay of X number of days. It’s thus entirely dependent upon the quantity and accuracy of your data” (Wood, 2019)
It also enables the detection of special areas — a risk incident, for example, with a high possibility of raising or a disastrous outcome. And it can be used to manage risk in real time at any point of the project. However, there is no doubt that a combined solution is better. They are basically two sections of a single whole, so that the ‘risk stage’ of each operation can be completely defined in the project schedule.
“It’s generally accepted that qualitative risk analysis is an older form of risk management than its quantitative counterpart. Not because human civilization’s earliest project managers had any particular bias towards the qualitative methodology; the answer is actually much simpler than that” (Wood, 2019)
One issue with qualitative evaluation is that those who conduct it are highly complex both in likelihood and in effect.HR consequences are more important than qualitative impacts for HR individuals , for example, and vice versa. In terms of a probability bias, a lack of understanding of the timeframes of other procedures can lead someone to believe that mistakes and failures occur more frequently in one’s own process than others.
While the quantitative risk evaluation, relies on factual and measurable data and highly statistical and analytical basis for estimating risks and impact values, usually expressing the risk value in monetary terms, rendering their findings useful beyond the framework of the evaluation.
“To reach a monetary result, quantitative risk assessment often makes use of these concepts:SLE (Single Loss Expectancy): money expected to be lost if the incident occurs one time.ARO (Annual Rate of Occurrence): how many times in a one-year interval the incident is expected to occur.ALE (Annual Loss Expectancy): money expected to be lost in one year considering SLE and ARO (ALE = SLE * ARO). For quantitative risk assessment, this is the risk value” (Leal, 2017)
As you can see, qualitative and quantitative tests have some characteristics that enhance each for a particular risk assessment situation, but incorporating both methods can, on the wide scale, prove to be the best alternative to a risk assessment. You can easily define most of the risks under normal circumstances with the use of the qualitative method. And the fears of people about their work can be used as a simple guide for evaluating these risks as important or not. You can then use the quantitative approach to relevant risks for more comprehensive decision-making details.
Risk evaluation is one of the most important and most difficult elements of risk management – individual, technological and administrative. When done correctly, the introduction of an ISO 27001 Information Security Management Framework could undermine any effort that organizations might make about the execution of qualitative or quantitative evaluations. However, you do not rely on a single methodology because ISO 27001 makes it possible to measure both qualitative and quantitative risk.
If you have you been involved with a company doing a redesign of business processes, discuss what went right during the redesign and what went wrong from your perspective. Additionally, provide a discussion on what could have been done better to minimize the risk of failure. If you have not yet been involved with a business process redesign, research a company that has recently completed one and discuss what went wrong, what went right, and how the company could have done a better job minimizing the risk of failure.
Your paper should meet the following requirements:
• Be approximately 5 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.
• Minimum 5 references required.
Some common biometric techniques include:
Select one of these biometric techniques and explain the benefits and the vulnerabilities associated with that method in 3-4 paragraphs.
– No Plagirism
– References required