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The readings this week discusses broad context of risk and investigative forensics. Part of risk management is to understand when things go wrong, we need to be able to investigate and report our findings to management. Using this research, or other research you have uncovered discuss in detail how risk and investigate techniques could work to help the organization. ERM helps to protect an organization before an attack, where as forensics investigate technique will help us after an attack – so lets discus both this week.

Please make your initial post should have 500 words and the post will do at least TWO of the following:

• 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)

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.

 Select from the following list four (4) topics and discuss. Use only 50-words max per topic to discuss and present your answer.  The discussion questions this week are from Chapter’s 16-18  (Jamsa, 2013).Chapter 16 topics:

• Define and describe total cost of ownership. List at least 10 items to consider when determining a data center’s total cost of ownership.
• Define and describe a capital expense. How are capital expenses different from operational expenses?
• Define and describe economies of scale and provide a cloud-based example.
• Define and describe “right sizing” as it pertains to cloud computing.
• Define Moore’s law and discus how it might influence cloud migration.
• Given company revenues of $2.5 million and expenses of $2.1 million, calculate the company’s profit and profit margin.

Chapter 17 topics:

• Compare and contrast functional and nonfunctional requirements and provide an example of each.
• Discuss why a designer should avoid selecting an implementation platform for as long as possible during the design process.
• Discuss various trade-offs a designer may need to make with respect to nonfunctional requirements.
• Discuss why the system maintenance phase is often the most expensive phase of the software development life cycle.

Chapter 18 topics:

• Using Yahoo! Pipes, create a pipe that displays the names of pizza restaurants within a given zip code.
• Using Google App Engine, create a page that displays the following Python script:
  • print “Content-type: text/htmlnn”
  • print “Cloud Computing, Chapter 18“

NOTE: You are required to use at least two-peer reviewed sources (besides your textbook) to answer the above questions.

 After reading chapter 13, analyze the advantages and disadvantages of digital signatures, please write one page(body) in APA format.

I need this entire pdf done no plagiarism. Please check out the document before inquiring!!!

 These instructions are compatible with both Microsoft Windows and Mac operating systems.

At Placer Hills Real Estate, commission is split with other agencies based on price groups. You create a one-variable data table to display results for various split rates. Additionally, you create scenarios for selling price and commission and create a histogram about sales.

[Student Learning Outcomes 9.1, 9.3, 9.4, 9.6]

File Needed: PlacerHills-09.xlsx (Available from the Start File link.)

Completed Project File Name: [your name]-PlacerHills-09.xlsx

Skills Covered in This Project

• Build a one-variable data table.
• Use Solver.
• Create and manage scenarios.
• Create a histogram with a chart.

1. Open the PlacerHills-09 start file. The file will be renamed automatically to include your name. Change the project file name if directed to do so by your instructor
2. Enable the content security warning.
3. Review formulas.
   1. Select cell C14 on the Calculator worksheet. The total commission is calculated by multiplying the selling price by the commission rate.
   2. Select cell C15. The IFS function checks the selling price (C12) to determine the split percentage (column D) based on the price group.
   3. Select cell C16. The IFS function checks the selling price (C12) to determine the administrative fee percentage (column E) and multiplies that percentage by the value in cell C15 to calculate the fee in dollars.
   4. Select cell C17. The net commission is calculated by subtracting the fees from the PHRE amount.
4. Build one-variable data tables.
   1. Select cell C20 and create a reference to cell C15.
   2. Select cell D20 and create a reference to cell C17. Both formulas depend on cell C12, the one variable.
   3. Use cell D8 as the column input for the data table (Figure 9-87). (You can use any percentage from column D because its value is replaced by the proposed rates in column B in the data table.)Figure 9-87 Data table setup for commission rates
   4. Decrease the decimal two times for all values in the data table.
5. Name cell ranges.
   1. Click the Price Solver worksheet tab.
   2. Click cell C12 and name the range Selling_Price. You cannot use spaces in a range name.
   3. Name cell C14 as Total_Commission and cell C17 as PHRE_Commission.
6. Install the Solver Add-in and the Analysis ToolPak.
7. Use Solver to find target PHRE net commission amounts.
   1. Build a Solver problem with cell C17 as the objective cell. For the first solution, set the objective to a value of 50000 by changing cell C12. Use the GRG Nonlinear solving method. Save the results as a scenario named $50,000.
   2. Restore the original values and run another Solver problem to find a selling price for a PHRE commission of 75000. Save these results as a scenario named $75,000.
   3. Restore the original values and run a third Solver problem to find a selling price for a net commission of $100,000. Save these results as a scenario and restore the original values.
8. Manage scenarios.
   1. Show the $50,000 scenario in the worksheet.
   2. Create a Scenario summary report for cells C12, C14, and C17.
9. Create a histogram for recent sales.
   1. Click the Sales Forecast sheet tab and select cell G13.
   2. Create a bin range of 10 values starting at 350,000 with intervals of 50,000, ending at 800,000 in cell G22.
   3. Use the Analysis ToolPak to create a histogram for cells E5:E26. Do not check the Labels box and select the bin range in your worksheet.
   4. Select cell H3 for the Output Range and include a chart.
   5. Position and size the chart from cell K3 to cell V19.
   6. Edit the horizontal axis title to display Selling Price and edit the vertical axis title to Number of Sales.
   7. Edit the chart title to display Sales by Price Group.
   8. Select and delete the legend.
   9. Delete cells G13:G22 (Figure 9-88).Figure 9-88 Histogram and chart for sales data
10. Save and close the workbook (Figure 9-89).
11. Upload and save your project file.
12. Submit project for grading.

Select ONE ARTICLE from the following links and summarize the reading in your own words. Your summary should be 2-3 paragraphs in length and uploaded as a TEXT DOCUMENT. 

http://topics.nytimes.com/top/reference/timestopics/subjects/c/computer_security/index.html

https://www.lifewire.com/learn-how-antivirus-4102748

http://www.sans.org/newsletters/

http://news.cnet.com/security/https://www.onlinesecurity.com/news–publications-pagehttp://www.esecurityplanet.com/viewshttp://netsecurity.about.com/
https://www.techrepublic.com/

 Please go through the attached document with instructions and requirements completely before providing the answer

Read Deceitful Spammer or Marketing Genius? and complete the questions at the end of the case study.

https://www.scu.edu/ethics/focus-areas/business-ethics/resources/deceitful-spammer-or-marketing-genius/

At least 600 words with APA format. Need to answer all questions. Reference and citations.

It is a priority that students are provided with strong educational programs and courses that allow them to be servant-leaders in their disciplines and communities, linking research with practice and knowledge with ethical decision-making. This assignment is a written assignment where students will demonstrate how this course research has connected and put into practice within their own career.

Assignment:

Provide a reflection of at least 500 words (or 2 pages double spaced) of how the knowledge, skills, or theories of this course have been applied, or could be applied, in a practical manner to your current work environment. If you are not currently working, share times when you have or could observe these theories and knowledge could be applied to an employment opportunity in your field of study.  

Requirements:

Use of proper APA formatting and citations. If supporting evidence from outside resources is used those must be properly cited.

Share a personal connection that identifies specific knowledge and theories from this course.

Demonstrate a connection to your current work environment. If you are not employed, demonstrate a connection to your desired work environment. 

You should not provide an overview of the assignments assigned in the course. The assignment asks that you reflect how the knowledge and skills obtained through meeting course objectives were applied or could be applied in the workplace.

M5 Assign 2

Use Decision Tree to find the relationship between dependent variable Y and independent variables X0 and X1 in the below data file. Upload a screenshot of the decision tree in the discussion.

Download data file: dummy_data_v2.xlsx Download dummy_data_v2.xlsx

Refer/use Decision_Tree_using_dummy_data.ipynb  Download Decision_Tree_using_dummy_data.ipynbfor code.

M6 Assign 1

Use the crowdfunding data Crowdfunding_data_1000_projects.xlsx Download Crowdfunding_data_1000_projects.xlsx.

(1) Select columns: Goal, students_reached, and funding_status and create a new data-frame. (1 point)

(2) Create random train and test data-frames in 75:25 ratio. (1 point)

(3) Using K-means, cluster the train data-frame into two clusters. Use Goal and students_reached columns (only independent variables) for clustering (4 points)

(4) Plot the scatter plots before and after clustering. (2 points)

(5) Use predict() function and predict cluster labels for test data-frame. (2 points)

Upload one Jupyter Notebook file for assignment submission.

M8 Assign 1

Q1. Collect tweets for the keyword ‘GoFundMe’. Store the following columns in a pandas data-frame (3 point).

(1) created_at

(2) id

(3) text

Q2 Store the pandas data-frame in an excel file without index (2).

Q3. Answer Q1 and Q2 for another keyword – UMSL. Use ‘UMSL’ as the keyword to search tweets (5 points).

Note: (1) You should write separate code for Q3; (2) The number of tweets is equal to the number of tweets received from Twitter using single use of the following code: tweets= api.search(“keyword”). Generally, it varies between 15 to 25)

Submit one Jupyter notebook file  and two excel files.

M8 Assign 2

Collect tweets  for the  keyword ‘Donorschoose’ and store in a pandas data-frame with the following columns:

(1) tweet_text (2) tweet_id (3) retweet_count and (4) place.

Upload a screenshot of the data-frame. You can either directly upload the screenshot as an image or paste it in a MS word file and upload the file.

(Note: The number of tweets is equal to the number of tweets received from Twitter using single use of the following code: tweets= api.search(“Donorschoose”). Generally, it varies between 15 to 25).

M10 Assign 1

Collect at least 100 tweets and perform following steps:

Linear Model (4 points / 1 point each)

1 (a). Extract retweet_count and followers_count from tweets.

1 (b). Perform train test split in 70:30 ratio where 70% of the data is stored in the train data-frame and remaining data is stored in the test data-frame.

1 (c).  Build linear model with train data-frame using retweet_count as dependent variable and followers_count as independent variable.

1 (d). Predict retweet_count on the test data-frame.

Decision Tree Model ( 6 points/1 point each)

2 (a). Extract retweet_count and followers_count and transform retweet_count to 1 if retweet_count>0 and 0 otherwise.

2 (b) Perform train test split in 70:30 ratio where 70% of the data is stored in train data-frame and remaining data is stored in test data-frame.

2 (c) Build decision tree model with train data-frame using retweet_count as dependent variable and followers_count as independent variable.

2 (d) Predict retweet_count on test data-frame.

2 (e) Show model accuracy.

2 (f) Show confusion matrix

M10 Assign 2

Collect at least 100 tweets and perform following steps:

1. Transform retweet_count to 1 if retweet_count>0 and 0 otherwise.

2. Build decision tree model with retweet_count as dependent variable and followers_count as independent variable.

3. Upload a decision tree plot using either matplotlib or Graphviz.

M11 Assign 1

(1) Follow  EC2 tutorial_v2_10_29_2021.pdf and create a VM using AWS EC2 service. Document each step using screenshots and description in a Microsoft (MS) Word file (5 points).

(2) Download and install Anaconda software on the VM and run Jupyter notebook to print “Hello World.” Document each step using screenshots and description in a MS Word file (5 points).

Submit one MS word file for the assignment.

After completing the assignment, you should terminate the VM following these steps: (1) select the VM (select checkbox) –> From the “Instance state” dropdown list, select “Terminate Instance”.