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Category: Computer Science

  • [Solved] 610 week 2

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    Using outsourced security personnel, what types of threats might this pose for an organization? What controls can be used to mitigate these threats? Cite all your sources, including the ones identified here (e.g., your course text), using APA format. If possible, cite external sources that help subs…

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    Using outsourced security personnel, what types of threats might this pose for an organization? What controls can be used to mitigate these threats?
    Cite all your sources, including the ones identified here (e.g., your course text), using APA format. If possible, cite external sources that help substantiate your position. Provide any available scholarly research, directives, publications, or memorandums to support your discussion and provide references in APA format.
    **The discussion assignment must have:**
    1. Proper grammar and APA formatting.
    2. Minimum 250 words for the first discussion post

  • [Solved] CCA 640: Cloud Computing Implementations and Migrations 2258

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    In-depth study guidance and analytical solution covering CCA 640: Cloud Computing Implementations and Migrations 2258.

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  • [Solved] Explain cloud computing in simple terms

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    In-depth study guidance and analytical solution covering Explain cloud computing in simple terms.

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  • [Solved] Computer Science Homework 2

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    – ## MMIS671_homework21.docx – hw2-1.q2.data1.zip **## MMIS671_homework21.docx** **Homework 2.** **Question 1. Decision Tree Classifier [10 Points]** Data: The zip file “ _hw2.q1.data.zip_” contains 3 CSV files: · “ _hw2.q1.train.csv_” contains 10,000 rows and 26 columns. The first column ‘ _y_’…

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    – ## MMIS671_homework21.docx

    – hw2-1.q2.data1.zip

    **## MMIS671_homework21.docx**

    **Homework 2.**

    **Question 1. Decision Tree Classifier [10 Points]**

    Data: The zip file “
    _hw2.q1.data.zip_” contains 3 CSV files:

    · “
    _hw2.q1.train.csv_” contains 10,000 rows and 26 columns. The first column ‘
    _y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
    _x_1_, …, x
    __25_.

    · “
    _hw2.q1.test.csv_” contains 2,000 rows and 26 columns. The first column ‘
    _y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
    _x_1_, …, x
    __25_.

    · “
    _hw2.q1.new.csv_” contains 30 rows and 26 columns. The first column ‘
    _ID_’ is an identifier for 30 unlabeled samples. The remaining 25 columns contain input features:
    _x_1_, …, x
    __25_.

    **Task 1**. [4 points]

    Use 5-fold cross-validation with the 10,000 labeled exampled from “
    _hw2.q1.train.csv_” to determine the fewest number of rules using which a decision tree classifier can achieve mean cross-validation accuracy of at least 0.96. Report the number of rules needed, the cross-validation accuracy obtained, and all the hyper-parameter values for the
    _DecisionTreeClassifier_.

    _Fewest number of rules needed_: ………………. (to achieve mean cross-validation accuracy of at least 0.96)

    _Mean cross-validation accuracy: ………………………._(
    _rounded to 4 decimal places_)

    _Non-default hHyper-parameter values for selected DecisionTreeClassifier model:_

    **Task 2**. [2 Points]

    Train a
    _DecisionTreeClassifier_ with the hyper-parameter values determined in Task 1 on all 10,000 training samples and use it to predict the output class ‘
    _y_’ for the 2,000 examples in “
    _hw2.q1.test.csv_”
    _._ Report the following:

    ·
    _Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)

    ·
    _Classification report_ for the 2,000 test examples:

    ·
    _Confusion matrix_ for the 2,000 test examples:

    **Task 3.**[2 Points]

    Use the model trained in Task 2 to predict the output class ‘
    _y’_ for the 30 examples in “
    _hw2.q1.new.csv_”. Specify the predicted classes in the table below:

    | | |
    | — | — |
    | ID | predicted y |
    | 1 | |
    | 2 | |
    | 3 | |
    | 4 | |
    | 5 | |
    | 6 | |
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    | 8 | |
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    | 29 | |
    | 30 | |

    **Task 4.**[2 Points]

    Of the 25 input variables which ones are relevant for this classification task?

    The following … input variables are relevant for this classification task: …………………

    Display your trained decision tree:

    **Question 2. Supervised machine learning classifiers [10 Points]**

    Data: The zip file “
    _hw2.q2.data.zip_” contains 3 CSV files:

    · “
    _hw2.q2.train.csv_” contains 8,000 rows and 11 columns. The first column ‘
    _y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
    _x1_, …, x
    _10_.

    · “
    _hw2.q2.test.csv_” contains 2,000 rows and 11 columns. The first column ‘
    _y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
    _x1_, …, x
    _10_.

    · “
    _hw2.q1.new.csv_” contains 30 rows and 10 columns. The first column ‘
    _ID_’ is an identifier for 30 unlabeled samples. The remaining 10 columns contain input features:
    _x1_, …, x
    _10_.

    **Task 1**. [6 points]

    Use 4-fold cross-validation with the 8,000 labeled exampled from “
    _hw2.q2.train.csv_” to identify a classifier that achieves mean cross-validation accuracy of at least 0.96. You should try several
    _Scikit-Learn_ classifiers, including:
    _GaussianNB, DecisionTreeClassifier, RandomForestClassifier, ExtraTreesClassifier, KNeighborsClassifier, LogisticRegression, SVC, and MLPClassifier_. Try different hyper-parameter values for the better performing classifiers to obtain a good set of hyper-parameter values. Then select the best performing model. Report the following:

    **_Selected model with hyper-parameter values_** _:_

    _Mean cross-validation accuracy: ………………………._(
    _rounded to 4 decimal places_)

    **Task 2**. [2 Points]

    Train the classifier with the hyper-parameter values determined in Task 1 on all 8,000 training samples and use it to predict the output class ‘
    _y_’ for the 2,000 examples in “
    _hw2.q2.test.csv_”
    _._ Report the following:

    ·
    _Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)

    ·
    _Classification report_ for the 2,000 test examples:

    ·
    _Confusion matrix_ for the 2,000 test examples:

    **Task 3.**[2 Points]

    Use the model trained in Task 2 to predict the output class ‘
    _y’_ for the 30 examples in “
    _hw2.q2.new.csv_”. Specify the predicted classes in the table below:

    | | |
    | — | — |
    | ID | predicted y |
    | ID_001 | |
    | ID_002 | |
    | ID_003 | |
    | ID_004 | |
    | ID_005 | |
    | ID_006 | |
    | ID_007 | |
    | ID_008 | |
    | ID_009 | |
    | ID_010 | |
    | ID_011 | |
    | ID_012 | |
    | ID_013 | |
    | ID_014 | |
    | ID_015 | |
    | ID_016 | |
    | ID_017 | |
    | ID_018 | |
    | ID_019 | |
    | ID_020 | |
    | ID_021 | |
    | ID_022 | |
    | ID_023 | |
    | ID_024 | |
    | ID_025 | |
    | ID_026 | |
    | ID_027 | |
    | ID_028 | |
    | ID_029 | |
    | ID_030 | |

    **hw2-1.q2.data1.zip**

    This file is too large to display. [View in new window](https://www.sweetstudy.com/files/hw2-1-q2-data1-zip)

    **hw2-1.q2.data1.zip**

    This file is too large to display. [View in new window](https://www.sweetstudy.com/files/hw2-1-q2-data1-zip)

    **## MMIS671_homework21.docx**

    **Homework 2.**

    **Question 1. Decision Tree Classifier [10 Points]**

    Data: The zip file “
    _hw2.q1.data.zip_” contains 3 CSV files:

    · “
    _hw2.q1.train.csv_” contains 10,000 rows and 26 columns. The first column ‘
    _y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
    _x_1_, …, x
    __25_.

    · “
    _hw2.q1.test.csv_” contains 2,000 rows and 26 columns. The first column ‘
    _y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
    _x_1_, …, x
    __25_.

    · “
    _hw2.q1.new.csv_” contains 30 rows and 26 columns. The first column ‘
    _ID_’ is an identifier for 30 unlabeled samples. The remaining 25 columns contain input features:
    _x_1_, …, x
    __25_.

    **Task 1**. [4 points]

    Use 5-fold cross-validation with the 10,000 labeled exampled from “
    _hw2.q1.train.csv_” to determine the fewest number of rules using which a decision tree classifier can achieve mean cross-validation accuracy of at least 0.96. Report the number of rules needed, the cross-validation accuracy obtained, and all the hyper-parameter values for the
    _DecisionTreeClassifier_.

    _Fewest number of rules needed_: ………………. (to achieve mean cross-validation accuracy of at least 0.96)

    _Mean cross-validation accuracy: ………………………._(
    _rounded to 4 decimal places_)

    _Non-default hHyper-parameter values for selected DecisionTreeClassifier model:_

    **Task 2**. [2 Points]

    Train a
    _DecisionTreeClassifier_ with the hyper-parameter values determined in Task 1 on all 10,000 training samples and use it to predict the output class ‘
    _y_’ for the 2,000 examples in “
    _hw2.q1.test.csv_”
    _._ Report the following:

    ·
    _Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)

    ·
    _Classification report_ for the 2,000 test examples:

    ·
    _Confusion matrix_ for the 2,000 test examples:

    **Task 3.**[2 Points]

    Use the model trained in Task 2 to predict the output class ‘
    _y’_ for the 30 examples in “
    _hw2.q1.new.csv_”. Specify the predicted classes in the table below:

    | | |
    | — | — |
    | ID | predicted y |
    | 1 | |
    | 2 | |
    | 3 | |
    | 4 | |
    | 5 | |
    | 6 | |
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    | 30 | |

    **Task 4.**[2 Points]

    Of the 25 input variables which ones are relevant for this classification task?

    The following … input variables are relevant for this classification task: …………………

    Display your trained decision tree:

    **Question 2. Supervised machine learning classifiers [10 Points]**

    Data: The zip file “
    _hw2.q2.data.zip_” contains 3 CSV files:

    · “
    _hw2.q2.train.csv_” contains 8,000 rows and 11 columns. The first column ‘
    _y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
    _x1_, …, x
    _10_.

    · “
    _hw2.q2.test.csv_” contains 2,000 rows and 11 columns. The first column ‘
    _y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
    _x1_, …, x
    _10_.

    · “
    _hw2.q1.new.csv_” contains 30 rows and 10 columns. The first column ‘
    _ID_’ is an identifier for 30 unlabeled samples. The remaining 10 columns contain input features:
    _x1_, …, x
    _10_.

    **Task 1**. [6 points]

    Use 4-fold cross-validation with the 8,000 labeled exampled from “
    _hw2.q2.train.csv_” to identify a classifier that achieves mean cross-validation accuracy of at least 0.96. You should try several
    _Scikit-Learn_ classifiers, including:
    _GaussianNB, DecisionTreeClassifier, RandomForestClassifier, ExtraTreesClassifier, KNeighborsClassifier, LogisticRegression, SVC, and MLPClassifier_. Try different hyper-parameter values for the better performing classifiers to obtain a good set of hyper-parameter values. Then select the best performing model. Report the following:

    **_Selected model with hyper-parameter values_** _:_

    _Mean cross-validation accuracy: ………………………._(
    _rounded to 4 decimal places_)

    **Task 2**. [2 Points]

    Train the classifier with the hyper-parameter values determined in Task 1 on all 8,000 training samples and use it to predict the output class ‘
    _y_’ for the 2,000 examples in “
    _hw2.q2.test.csv_”
    _._ Report the following:

    ·
    _Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)

    ·
    _Classification report_ for the 2,000 test examples:

    ·
    _Confusion matrix_ for the 2,000 test examples:

    **Task 3.**[2 Points]

    Use the model trained in Task 2 to predict the output class ‘
    _y’_ for the 30 examples in “
    _hw2.q2.new.csv_”. Specify the predicted classes in the table below:

    | | |
    | — | — |
    | ID | predicted y |
    | ID_001 | |
    | ID_002 | |
    | ID_003 | |
    | ID_004 | |
    | ID_005 | |
    | ID_006 | |
    | ID_007 | |
    | ID_008 | |
    | ID_009 | |
    | ID_010 | |
    | ID_011 | |
    | ID_012 | |
    | ID_013 | |
    | ID_014 | |
    | ID_015 | |
    | ID_016 | |
    | ID_017 | |
    | ID_018 | |
    | ID_019 | |
    | ID_020 | |
    | ID_021 | |
    | ID_022 | |
    | ID_023 | |
    | ID_024 | |
    | ID_025 | |
    | ID_026 | |
    | ID_027 | |
    | ID_028 | |
    | ID_029 | |
    | ID_030 | |

    **hw2-1.q2.data1.zip**

    This file is too large to display. [View in new window](https://www.sweetstudy.com/files/hw2-1-q2-data1-zip)

    **## MMIS671_homework21.docx**

    **Homework 2.**

    **Question 1. Decision Tree Classifier [10 Points]**

    Data: The zip file “
    _hw2.q1.data.zip_” contains 3 CSV files:

    · “
    _hw2.q1.train.csv_” contains 10,000 rows and 26 columns. The first column ‘
    _y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
    _x_1_, …, x
    __25_.

    · “
    _hw2.q1.test.csv_” contains 2,000 rows and 26 columns. The first column ‘
    _y_’ is the output variable with 2 classes: 0, 1. The remaining 25 columns contain input features:
    _x_1_, …, x
    __25_.

    · “
    _hw2.q1.new.csv_” contains 30 rows and 26 columns. The first column ‘
    _ID_’ is an identifier for 30 unlabeled samples. The remaining 25 columns contain input features:
    _x_1_, …, x
    __25_.

    **Task 1**. [4 points]

    Use 5-fold cross-validation with the 10,000 labeled exampled from “
    _hw2.q1.train.csv_” to determine the fewest number of rules using which a decision tree classifier can achieve mean cross-validation accuracy of at least 0.96. Report the number of rules needed, the cross-validation accuracy obtained, and all the hyper-parameter values for the
    _DecisionTreeClassifier_.

    _Fewest number of rules needed_: ………………. (to achieve mean cross-validation accuracy of at least 0.96)

    _Mean cross-validation accuracy: ………………………._(
    _rounded to 4 decimal places_)

    _Non-default hHyper-parameter values for selected DecisionTreeClassifier model:_

    **Task 2**. [2 Points]

    Train a
    _DecisionTreeClassifier_ with the hyper-parameter values determined in Task 1 on all 10,000 training samples and use it to predict the output class ‘
    _y_’ for the 2,000 examples in “
    _hw2.q1.test.csv_”
    _._ Report the following:

    ·
    _Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)

    ·
    _Classification report_ for the 2,000 test examples:

    ·
    _Confusion matrix_ for the 2,000 test examples:

    **Task 3.**[2 Points]

    Use the model trained in Task 2 to predict the output class ‘
    _y’_ for the 30 examples in “
    _hw2.q1.new.csv_”. Specify the predicted classes in the table below:

    | | |
    | — | — |
    | ID | predicted y |
    | 1 | |
    | 2 | |
    | 3 | |
    | 4 | |
    | 5 | |
    | 6 | |
    | 7 | |
    | 8 | |
    | 9 | |
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    | 23 | |
    | 24 | |
    | 25 | |
    | 26 | |
    | 27 | |
    | 28 | |
    | 29 | |
    | 30 | |

    **Task 4.**[2 Points]

    Of the 25 input variables which ones are relevant for this classification task?

    The following … input variables are relevant for this classification task: …………………

    Display your trained decision tree:

    **Question 2. Supervised machine learning classifiers [10 Points]**

    Data: The zip file “
    _hw2.q2.data.zip_” contains 3 CSV files:

    · “
    _hw2.q2.train.csv_” contains 8,000 rows and 11 columns. The first column ‘
    _y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
    _x1_, …, x
    _10_.

    · “
    _hw2.q2.test.csv_” contains 2,000 rows and 11 columns. The first column ‘
    _y_’ is the output variable with 4 classes: 0, 1, 2, 3. The remaining 10 columns contain input features:
    _x1_, …, x
    _10_.

    · “
    _hw2.q1.new.csv_” contains 30 rows and 10 columns. The first column ‘
    _ID_’ is an identifier for 30 unlabeled samples. The remaining 10 columns contain input features:
    _x1_, …, x
    _10_.

    **Task 1**. [6 points]

    Use 4-fold cross-validation with the 8,000 labeled exampled from “
    _hw2.q2.train.csv_” to identify a classifier that achieves mean cross-validation accuracy of at least 0.96. You should try several
    _Scikit-Learn_ classifiers, including:
    _GaussianNB, DecisionTreeClassifier, RandomForestClassifier, ExtraTreesClassifier, KNeighborsClassifier, LogisticRegression, SVC, and MLPClassifier_. Try different hyper-parameter values for the better performing classifiers to obtain a good set of hyper-parameter values. Then select the best performing model. Report the following:

    **_Selected model with hyper-parameter values_** _:_

    _Mean cross-validation accuracy: ………………………._(
    _rounded to 4 decimal places_)

    **Task 2**. [2 Points]

    Train the classifier with the hyper-parameter values determined in Task 1 on all 8,000 training samples and use it to predict the output class ‘
    _y_’ for the 2,000 examples in “
    _hw2.q2.test.csv_”
    _._ Report the following:

    ·
    _Accuracy on 2,000 test examples_: …………………… (rounded to 4 decimal places)

    ·
    _Classification report_ for the 2,000 test examples:

    ·
    _Confusion matrix_ for the 2,000 test examples:

    **Task 3.**[2 Points]

    Use the model trained in Task 2 to predict the output class ‘
    _y’_ for the 30 examples in “
    _hw2.q2.new.csv_”. Specify the predicted classes in the table below:

    | | |
    | — | — |
    | ID | predicted y |
    | ID_001 | |
    | ID_002 | |
    | ID_003 | |
    | ID_004 | |
    | ID_005 | |
    | ID_006 | |
    | ID_007 | |
    | ID_008 | |
    | ID_009 | |
    | ID_010 | |
    | ID_011 | |
    | ID_012 | |
    | ID_013 | |
    | ID_014 | |
    | ID_015 | |
    | ID_016 | |
    | ID_017 | |
    | ID_018 | |
    | ID_019 | |
    | ID_020 | |
    | ID_021 | |
    | ID_022 | |
    | ID_023 | |
    | ID_024 | |
    | ID_025 | |
    | ID_026 | |
    | ID_027 | |
    | ID_028 | |
    | ID_029 | |
    | ID_030 | |

    **hw2-1.q2.data1.zip**

    This file is too large to display. [View in new window](https://www.sweetstudy.com/files/hw2-1-q2-data1-zip)

    – 1
    – 2

  • [Solved] kp Final por

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  • [Solved] In Basketball Stars, a player attempts 25 shots in one game.

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    In [basketball stars](https://basketball-stars.io/), a player attempts 25 shots in one game. a) If 15 shots are successful, what is the player’s shooting percentage? b) The next game, the player makes 18 out of 30 shots. Compare the two shooting percentages. c) What is the overall shooting percentag…

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    Assignment Prompt & Instructions

    In  [basketball stars](https://basketball-stars.io/), a player attempts 25 shots in one game.
    a) If 15 shots are successful, what is the player’s shooting percentage?
    b) The next game, the player makes 18 out of 30 shots. Compare the two shooting percentages.
    c) What is the overall shooting percentage across both games combined?”

  • [Solved] Test 1

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    In-depth study guidance and analytical solution covering Test 1.

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  • [Solved] problem

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    – ## Chap1problem.docx **## Chap1problem.docx** **Research problems due 9/18** **Please follow the instructions carefully for your research problem. Your argument and research input will significantly impact your grade. Ensure that you check for AI-generated content and plagiarism before submitting …

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    – ## Chap1problem.docx

    **## Chap1problem.docx**

    **Research problems due 9/18**

    **Please follow the instructions carefully for your research problem. Your argument and research input will significantly impact your grade. Ensure that you check for AI-generated content and plagiarism before submitting your paper. AI-generated content should not exceed 10%, and content from external sources should be limited to 10%**

    **Problem A.**

    1. Find a catalog or visit the Web site of a major distributor of microcomputer equipment, such as BestBuy or Dell (www.dell.com). Select or configure a system that provides optimal performance for the following types of users:

    · A home user who uses word-processing software, such as Microsoft Office; a home accounting package, such as Quicken or TurboTax; children’s games; and multimedia software for editing pictures and creating video DVDs

    · An accountant who uses office software, such as Microsoft Office, and statistical software, such as SPSS or SAS, and downloads large amounts of financial data from a corporate server for statistical and financial modeling

    · An architect who uses typical office software and CAD software, such as AutoCAD. Pay particular attention to whether CPU power, memory, disk space, and I/O capabilities are adequate. Compare the cost of each computer. Which is most expensive and why?

    2. Research current computer hardware offerings representative models for each computer class/type.

    3. Investigate one or more of the top-ranked supercomputers listed at www.top500.org. In what way(s) is each an example of multicomputer configuration? What are the typical specs of each individual computing node? How do those specs compare to typical midrange and mainframe computers?

    **Problem B**

    1. You’re an IS professional, and your boss has asked you to prepare a briefing for senior staff on the comparative advantages and disadvantages of three competing secondary storage technologies: magnetic hard disk drives, solid-state drives using single-level cell flash memory, and solid-state drives using multilevel cell flash memory. Search the digital libraries and technology Web sites listed in this chapter for source material to help you prepare the briefing. Which sites provide the most useful information? Which sites enable you to find useful information easily? Which sites have the most advertising? Which sites seem to con-tain biased information and what is the nature of the bias?

    2. Read a few dozen job listings at http://itjobs.computerworld.com or a similar site. Which jobs (and how many) require a broad knowledge of computer technology? At what level are these jobs? Try searching the listings based on a few specific keywords, such as architect, developer, and network. Examine the companies that are hiring and where their job postings show up in search results. Can you draw any conclusions about how the listings are ordered?

    **## Chap1problem.docx**

    **Research problems due 9/18**

    **Please follow the instructions carefully for your research problem. Your argument and research input will significantly impact your grade. Ensure that you check for AI-generated content and plagiarism before submitting your paper. AI-generated content should not exceed 10%, and content from external sources should be limited to 10%**

    **Problem A.**

    1. Find a catalog or visit the Web site of a major distributor of microcomputer equipment, such as BestBuy or Dell (www.dell.com). Select or configure a system that provides optimal performance for the following types of users:

    · A home user who uses word-processing software, such as Microsoft Office; a home accounting package, such as Quicken or TurboTax; children’s games; and multimedia software for editing pictures and creating video DVDs

    · An accountant who uses office software, such as Microsoft Office, and statistical software, such as SPSS or SAS, and downloads large amounts of financial data from a corporate server for statistical and financial modeling

    · An architect who uses typical office software and CAD software, such as AutoCAD. Pay particular attention to whether CPU power, memory, disk space, and I/O capabilities are adequate. Compare the cost of each computer. Which is most expensive and why?

    2. Research current computer hardware offerings representative models for each computer class/type.

    3. Investigate one or more of the top-ranked supercomputers listed at www.top500.org. In what way(s) is each an example of multicomputer configuration? What are the typical specs of each individual computing node? How do those specs compare to typical midrange and mainframe computers?

    **Problem B**

    1. You’re an IS professional, and your boss has asked you to prepare a briefing for senior staff on the comparative advantages and disadvantages of three competing secondary storage technologies: magnetic hard disk drives, solid-state drives using single-level cell flash memory, and solid-state drives using multilevel cell flash memory. Search the digital libraries and technology Web sites listed in this chapter for source material to help you prepare the briefing. Which sites provide the most useful information? Which sites enable you to find useful information easily? Which sites have the most advertising? Which sites seem to con-tain biased information and what is the nature of the bias?

    2. Read a few dozen job listings at http://itjobs.computerworld.com or a similar site. Which jobs (and how many) require a broad knowledge of computer technology? At what level are these jobs? Try searching the listings based on a few specific keywords, such as architect, developer, and network. Examine the companies that are hiring and where their job postings show up in search results. Can you draw any conclusions about how the listings are ordered?

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