Course Reflection:

We looked at several topics on global IT strategies, technologies, models, and networking during this session.As you get closer to starting your own dissertation, you will need to choose a topic in your first dissertation class, DSRT-736, so it is essential to start preparing. This week, let us take a look at some topics to consider, and by the end of the week, we could have several ideas for dissertation topicsSince you have already examined several research articles, another way would be to examine previous dissertations in these areas.Visit the University of Cumberland’s library, go to the Dissertation Database, and locate an interesting topic on global IT.Here are some pointers that will help critically evaluate some viable topics.

• Is the topic attainable for a first-time dissertation student?
• Is the problem rooted in the literature?
• Is the research empirical, i.e., is there a survey, is there an interview guide, has the data been analyzed via some statistical tool?
• Is there a theoretical model or framework discussed?

Discuss the topic, the problem the model has been used in the research, and any present findings.Do not read the entire dissertation, as the abstract and chapter one introduction should give a clear understanding of the research.

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”. 

CSE 461 Project I

The first part of this project requires that you implement a class that will be used to simulate a disk drive. The disk drive will have numberofblocks many blocks where each block has blocksize many bytes. The interface for the class Sdisk should include :

Class Sdisk



{



public :



Sdisk(string diskname, int numberofblocks, int blocksize);



int getblock(int blocknumber, string& buffer);



int putblock(int blocknumber, string buffer);



int getnumberofblocks(); // accessor function



int getblocksize(); // accessor function



private :



string diskname;        // file name of software-disk



int numberofblocks;     // number of blocks on disk



int blocksize;          // block size in bytes



};

An explanation of the member functions follows :

• Sdisk(diskname, numberofblocks, blocksize) This constructor incorporates the creation of the disk with the “formatting” of the device. It accepts the integer values numberofblocks, blocksize, a string diskname and creates a Sdisk (software-disk). The Sdisk is a file of characters which we will manipulate as a raw hard disk drive. The function will check if the file diskname exists. If the file exists, it is opened and treated as a Sdisk with numberofblocks many blocks of size blocksize. If the file does not exist, the function will create a file called diskname which contains numberofblocks*blocksize many characters. This file is logically divided up into numberofblocks many blocks where each block has blocksize many characters. The text file will have the following structure : Bytes 0 – blocksize-1Block 0Bytes blocksize – 2*blocksize-1Block 1Bytes 2*blocksize – 3*blocksize-1Block 2……Bytes ?-?Block numberofblocks-1
• getblock(blocknumber,buffer) retrieves block blocknumber from the disk and stores the data in the string buffer. It returns an error code of 1 if successful and 0 otherwise.
• putblock(blocknumber,buffer) writes the string buffer to block blocknumber. It returns an error code of 1 if successful and 0 otherwise.

IMPLEMENTATION GUIDELINES : It is essential that your software satisfies the specifications. These will be the only functions (in your system) which physically access the Sdisk. NOTE that you must also write drivers to test and demonstrate your program.

1. You will produce a formal closeout report.

Your assignment will have a formal cover page as per your previous assignments.

The second page will consist of a formal cover for a closeout report. This formal project closeout report cover will contain the following:

• Project name in Full:
• Date:
• Project Manager/Director:
• Document control form:

2. Produce a 1 page Executive Summary: (see thisthis websiteor other websites for ideas)

3. Purpose of the Project: (Differs from executive summary – Provide a brief 1 page overview of the purpose of the project in the case study).

4. Project Deliverables: (Document the deliverables you specifically identified in the case study and provide the date delivered).

• Deliverable
• Date Delivered

5. Post Implementation Support: (e.g. Field Support) – (What processes, tools, systems, documentation, FAQ sheets are in place to support the ongoing operating environment?)

6. Training: (What, if any, training has been developed for this project? Is training being handled through another project/program, if so what is the plan? How will you ensure the project deliverables are assimilated into the organization?)

7. Scope Change History: (If formal change management was used in your project and changes occurred, please document each change in a table using the following headings.)

• Change Request #
• Date of Request
• Title/Description
• Explanation

8. Lessons Learned:

• What went well in the project?
• What did not go well in the project?
• What should we do differently next time?

9. Provide a response memo to Dr. Knowall (this response memo is part of your total 8 – 10 pages).

Ensure that you provide documentation and rationale on what you will do to resolve the issues that Dr. Knowall has identified in his memo found in the Case Study. Please refer to the first part of the case study introduced in Unit 6 and the EVM analysis from Unit 7 as further information to support your project close out report. Dates for the deliverables etc. can be fictional to enhance the deliverable.

Include all other items that your team believes should be found in a Close Out Report for this Case Study as part of your final assignment submission.

Requirements: 1900 words

Selecting appropriate monitoring tools for a virtual environment is a very important responsibility of the System Administrators. For this discussion research applications from two (2) different vendors offering monitoring tools for a virtual environment. Evaluate at least one (1) application from each vendor and answer the following questions:

1. List the main features of each product.
2. What type of monitoring is being performed?
3. Provide a real-world example of how and where these products are being used
4. What would you monitor in a large, enterprise with over 1000 VMs?

You are NOT limited to the Hyper-V tools but can and should expand your research to include monitoring tools designed for other virtualization environments, including VMware vSphere and Citrix XenServer.

Please DO NOT pick the same products, if it was already selected and described by other students. This will ensure much better discussion and learning opportunities.

Provide references to support your statements and conclusions. Do not copy and paste from the internet. Instead, include URLs to original sources so that anyone can get more information.