Identify, define, and describe the three or four most important elements of an Information Security?
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Identify, define, and describe the three or four most important elements of an Information Security Plan. Be sure to identify and support why your selected elements are the most important. Minimum 3-5 pages with 6th edition APA format.
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University of the Cumberlands Inferential Statistics in Decision Making Paper
Question 1- A department chair in an academic department wanted to know if there was a difference between spring and fall ...
University of the Cumberlands Inferential Statistics in Decision Making Paper
Question 1- A department chair in an academic department wanted to know if there was a difference between spring and fall enrollment in his department. The department offered the same six courses both semesters. What test should he conduct to determine if there is a significant difference in the enrollment between the semesters? Write up the results and determine what do the results mean? Here is the data set: Spring Fall 30 50 35 28 40 30 45 39 50 40 55 25 Question 2- A researcher wants to know if there is a difference between student's mid-term exam scores and final exam scores in a business course. She has both sets of scores from her spring semester for one section of the class. What test should she conduct? Choose the correct answer. A-Descriptive Statistics B-ANOVA-One Way C-Paired samples t-test D-Independent samples t-test Question 3-What is the correct way to write the following t-test results and interpret the data? Choose the right answer. A B Mean 40.625 61.66667 Variance 504.5535714 462.6667 Observations 8 6 Pooled Variance 487.1006944 Hypothesized Mean Difference 0 df 12 t Stat -1.76533594 P(T<=t) one-tail 0.051458003 t Critical one-tail 1.782287556 P(T<=t) two-tail 0.102916007 t Critical two-tail 2.17881283 A- There is a significant difference between the means of A (M=40.63) and B (M=61.67), (t [12] = 1.76, p < .05). B-There is no significant difference between the means of A (M=40.63) and B (M=61.67), (t [12] = 1.76, p < .05). C-There is no significant difference between the means of A (M=40.63) and B (M=61.67), (t [12] = 1.76, p > .05). D-There is a significant difference between the means of A (M=40.63) and B (M=61.67), (t [12] = 2.18, p > .05). Question 4- A researcher wanted to know if there was a significant difference in cyber security breaches between cities on two different continents. What test should he conduct to determine if there is a significant difference in cyber security breaches between the continents? Write up the results and determine what do the results mean? Here is the data set: Asia Europe 17 20 25 30 26 15 27 14 28 12 50
CU Balance Scorecards Impact Knowledge Creation Culture and Strategy Essay
Information Systems for Business and Beyond Questions:
Chapter 6 – study questions 1-10, Exercise 3, 6, & 7
Informat ...
CU Balance Scorecards Impact Knowledge Creation Culture and Strategy Essay
Information Systems for Business and Beyond Questions:
Chapter 6 – study questions 1-10, Exercise 3, 6, & 7
Information Technology and Organizational Learning Assignment:
Chapter 6 – Review the section on knowledge creation, culture, and strategy. Explain how balance scorecards impact knowledge creation, culture, and strategy. Why are these important concepts to understand within an organization?
Note the first assignment should be in one section and the second section should have the information from the Information Technology and Organizational Learning assignment. The paper requirements for the two-pages applies to the second part of the assignment directly related to the Information Technology and Organizational Learning assignment.
Assignment 1: Disaster Recovery Plan
Assignment 1: Disaster Recovery PlanDue Week 6 and worth
120 points
You are an administrator for the Contoso Corporatio ...
Assignment 1: Disaster Recovery Plan
Assignment 1: Disaster Recovery PlanDue Week 6 and worth
120 points
You are an administrator for the Contoso Corporation. The Contoso Corporation
has two (2) large sites. The corporate office is in New York and the
manufacturing site is in Cleveland, Ohio. In addition, the Contoso Corporation
has five (5) smaller sites.
At the Corporate site, you currently have eight (8) physical Hyper-V hosts
that host approximately one hundred twenty (120) virtual machines. The virtual
machines are running Windows Server 2008 R2 and Windows Server 2012. The servers
are needed to host email, various databases, file services, print services, and
a point-of sales-application. The virtual machines are stored on a centrally
located storage area network (SAN) that has multiple power supplies, and use
redundant array of independent disks (RAID).
Over the last three (3) years, you have had two (2) power failures that
caused the servers to go down at the corporate office. Recently, during one (1)
of these incidents, files on one (1) of the servers were corrupted. It was later
discovered that the backup could only partially restore the data files, but
could not restore the server itself.
The servers must remain available and reliable. As the administrator, you
must ensure that you can recover from a failure or problem quickly with minimum
loss.
Write a three to five (3-5) page paper in which you:
Develop a continuity plan / disaster recovery plan for Contoso Corporation
in which you address the following:
Suggest an approach that provides redundancy (failover) for all mission
critical functions in the event of a disaster including power failure. Consider
the following in your approach: Essential Infrastructure; Email; Database; File
Services; Print Services; Point-of-Sale Applications; Power Redundancy
Solutions.
Identify the changes that should be made to the backup procedures in order
to address the problem with the server backups.
Outline the necessary actions that Server Administrators must verify for
proper operation of servers in the event of a failover.
Provide a plan for returning all services back to the primary systems once
the crisis has passed.
Examine the pros and cons of moving part of the data center to the Cleveland
office and housing it there on a permanent basis. Provide your recommendation
for the stated action.
Your assignment must follow these formatting requirements:
Be typed, double spaced, using Times New Roman font (size 12), with one-inch
margins on all sides; citations and references must follow APA or
school-specific format. Check with your professor for any additional
instructions.
Include a cover page containing the title of the assignment, the student’s
name, the professor’s name, the course title, and the date. The cover page and
the reference page are not included in the required assignment page
length.
The specific course learning outcomes associated with this assignment
are:
Demonstrate the ability to describe, manage, and configure load balancing
and failover clustering.
Demonstrate the ability to configure and manage backups to include
role-specific and online or cloud environments.
Demonstrate the ability to recover servers to include files and
volumes.
Use technology and information resources to research issues in advanced
network infrastructure environments.
Write clearly and concisely about advanced network infrastructure topics
using proper writing mechanics and technical style conventions.
University of Phoenix Manage Your Health Inc Recreation & Wellness Intranet Worksheet
Assignment ContentA project’s Work Breakdown Structure (WBS) and Gantt chart provide structure for a project. In this we ...
University of Phoenix Manage Your Health Inc Recreation & Wellness Intranet Worksheet
Assignment ContentA project’s Work Breakdown Structure (WBS) and Gantt chart provide structure for a project. In this week’s assignment you develop both the WBS and Gantt chart for a fictitious organization. The Manage Your Health, Inc (MYH) scenario will be used in the weekly assignments for the rest of the course.Review the Manage Your Health Scenario and follow the directions below for completing a WBS and Gantt chart.Develop a work breakdown structure (WBS) for the project. Break down the work to Level 3 or Level 4, as appropriate. Use the Work Breakdown Structure Template and Work Breakdown Structure example in this text as guides. Upload the WBS in list form. The WBS should be based on the information that would be in a project scope of this scenario. You can review your project plan from Wk 1. Create a Gantt chart using the WBS you developed with Microsoft Excel or another software of your choice and approved by your instructor. Do not enter any durations or dependencies. Submit 2 deliverables: the WBS and Gantt Chart.
Assignment 1: Disaster Recovery Plan
Assignment 1: Disaster Recovery PlanDue Week 6 and worth 120 pointsYou are an administrator for the Contoso Corporation. T ...
Assignment 1: Disaster Recovery Plan
Assignment 1: Disaster Recovery PlanDue Week 6 and worth 120 pointsYou are an administrator for the Contoso Corporation. The Contoso Corporation has two (2) large sites. The corporate office is in New York and the manufacturing site is in Cleveland, Ohio. In addition, the Contoso Corporation has five (5) smaller sites. At the Corporate site, you currently have eight (8) physical Hyper-V hosts that host approximately one hundred twenty (120) virtual machines. The virtual machines are running Windows Server 2008 R2 and Windows Server 2012. The servers are needed to host email, various databases, file services, print services, and a point-of sales-application. The virtual machines are stored on a centrally located storage area network (SAN) that has multiple power supplies, and use redundant array of independent disks (RAID).Over the last three (3) years, you have had two (2) power failures that caused the servers to go down at the corporate office. Recently, during one (1) of these incidents, files on one (1) of the servers were corrupted. It was later discovered that the backup could only partially restore the data files, but could not restore the server itself.The servers must remain available and reliable. As the administrator, you must ensure that you can recover from a failure or problem quickly with minimum loss. Write a three to five (3-5) page paper in which you:Develop a continuity plan / disaster recovery plan for Contoso Corporation in which you address the following:Suggest an approach that provides redundancy (failover) for all mission critical functions in the event of a disaster including power failure. Consider the following in your approach: Essential Infrastructure; Email; Database; File Services; Print Services; Point-of-Sale Applications; Power Redundancy Solutions.Identify the changes that should be made to the backup procedures in order to address the problem with the server backups.Outline the necessary actions that Server Administrators must verify for proper operation of servers in the event of a failover.Provide a plan for returning all services back to the primary systems once the crisis has passed.Examine the pros and cons of moving part of the data center to the Cleveland office and housing it there on a permanent basis. Provide your recommendation for the stated action. Your assignment must follow these formatting requirements:Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.The specific course learning outcomes associated with this assignment are:Demonstrate the ability to describe, manage, and configure load balancing and failover clustering.Demonstrate the ability to configure and manage backups to include role-specific and online or cloud environments.Demonstrate the ability to recover servers to include files and volumes.Use technology and information resources to research issues in advanced network infrastructure environments.Write clearly and concisely about advanced network infrastructure topics using proper writing mechanics and technical style conventions.
Complete python project
Project Part I (total 50 points), has three parts:I(a), 18 pointsI(b), 25 pointsProject Part I (total 50 points), has thre ...
Complete python project
Project Part I (total 50 points), has three parts:I(a), 18 pointsI(b), 25 pointsProject Part I (total 50 points), has three parts:I(a), 18 pointsI(b), 25 pointsI(c), 17 pointsRefer to blackboard for Due Date information.Important Notes (please read before starting the projects):1.Student is required to finish the project independently. For details, refer to the syllabus.2.All the homework & project are required to be written in python code and submitted in Jupyter Notebook (.ipynb) with Outputs/Results (“Run all” and “save”). If Outputs/Results are not shown, I will try to run the code from my end. If there is an error, any code/comments before the error will be graded. However, any code/comments after the error will be scored started from the basis of zero with the instructor’s best judgment for any merit, and the scores will be final without further negotiation[1].3.The submission can be one consolidated .ipynb (together with other files) or multiple .ipynb(s) as you see fit.4.Do not alter the original input data. Students may not get full points if the original input data is altered outside Python/Jupyter Notebook. For example, if the inputs are provided as five csv files, the requirement is to read in all the input files in Python/Jupyter Notebook as it is. (Partial) Points will be deducted if the five files were processed outside Python/Jupyter Notebook such as Excel before read in the files.5.If you would like to work on project part II for bonus points, you must submit part I as final and send me an email requesting the part II. Students are not allowed to submit part II without submit part I as final.6.The project assignment may require some levels of research effort, including online article/code (main source), research paper as well as textbook research.7.Updates to the project will be published in Blackboard, please pay attention to blackboard announcement. Updates/ Blackboard announcement will be mostly for clarification purpose.8.If there is any question, please email me.undefinedProject Part (a) Data Analysis: Triangular Arbitrage for Cryptocurrency (18 points)Introduction:A cryptocurrency is a digital or virtual currency that uses cryptography for security. The “crypto” in cryptocurrencies refers to complicated cryptography which allows for a particular digital token to be generated, stored, and transacted securely and, typically, anonymously. Alongside this important “crypto” feature of these currencies is a common commitment to decentralization. Many cryptocurrencies are decentralized systems based on “blockchain” technology, a distributed ledger enforced by a disparate network of computers.The first blockchain-based cryptocurrency was “Bitcoin”, which still remains the most popular and most valuable. Today, there are thousands of alternate cryptocurrencies with various functions or specifications. Some of these are clones of Bitcoin while others are forks, or new cryptocurrencies that split off from an already existing one.For more about cryptocurrency, please refer the Investopedia.Triangular arbitrage of cryptocurrency:Consider a collection of three (3) cryptocurrencies {BTC, ETH, LTC[2]} along with USD (US dollars). We would like to test for triangular arbitrage opportunities. Triangular arbitrage opportunities means one should be able to take in any initial capital (in any of the currencies), and return the resulting amount after a cycle (i.e. A->B->C->A). For instance, one can start with a given amount of USD, exchange for BTC, and then ETH, and then back to USD. There are several more combinations (e.g. start with BTC/ETH/LTC instead).At any point in time, we observe the bitcoin price in terms of USD. This price is denoted by BTC-USD. You can find it here BTC to USD. At the same time, one can derive the value of BTC-USD implied by ETH. Here’s how it works: Observe both ETH-USD and ETH-BTC, then their ratio should represent BTC-USD. Here’s a real example: On March 10, 2019, at the same exchange, Brittrex, this is what we observe. As seen below, the derived BTC-USD (computed by division) is different from the observed BTC-USD.ETH-USD135.27derived BTC-USD3915.6487ETH-BTC0.034546observed BTC-USD3915.3000You can find more prices here: ETH to BTC; ETH to USD; BTC to USD Project tasks (i) (6 points) Using the available historical data provided (the included csv files[3], date range: 3/10/2018--3/10/2019), plot the time series (line charts) of the two BTC-USD prices (derived and observed) on the same figure over this period. Note to use the “Price” column in each “csv” data instead of “Open”, “High” “Low” columns. Also note the date column is in reverse order, you may or may not need to sort it.(ii) (6 points) Compute the spread (i.e. difference between the two time series) and plot its time series.Compute the mean and standard deviation during this period (3/10/2018--3/10/2019). Also, show the histogram of the daily spreads to see its distribution.(iii) (6 points) Now repeat parts (i)-(ii) for Litecoin (LTC). That is, replace ETH above with LTC. You’ll need LTC-USD, LTC-BTC, and BTC-USD.Note: you may find the relevant prices using these links: link1, link2, link3(The links are provided for information purpose. All the input data has been provided as csv files)If you think further, you can replace LTC with other coins, and also BTC with another major crypto or Stablecoin. There are truly numerous combinations for triangular arbitrage in the crypto market! (Note: not your task)Hint1: Your output should be similar or identical to the figures in “I(a) Sample Output” folder. Feel free to create your own plot titles and labels as you see fit.Hint2: One of the input column has string dollar amount value similar to “4,000”. You will need to convert to numeric value 4000 before plot charts. You may consider replace or other functions to remove the comma in “4,000”.Project Part (b) Logistic Modeling (25 points)Logistic regression, also called a logit model, is used to model dichotomous outcome variables. In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables. Here we are going to use what we learned from the class (mainly Lecture 05, and also Lecture 06, 07) to help a researcher build a logistic regression model.The researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable. There are three predictor variables: GRE, GPA scores and “rank”. We will treat the variables GRE and GPA as continuous. The variable “rank” takes on the values 1 through 4. Institutions with a rank of 1 have the highest prestige, while those with a rank of 4 have the lowest. Use all the three variables to build logistic model.Using the data “Graduate_School_Admission.csv” provided.Project tasks(i) (3 points) Mimic what we learned in class, provide at least three example codes of Exploratory Data Analysis (“EDA”). “df.describe()” could serve as one example of EDA since it provides the count, mean, standard deviation and other information for the numeric variables. Run the codes and output results.(optional) provide brief interpretation of the EDA results(ii) (6 points) Split the data into training data and testing data. (splitting ratio is 4:1, in other word, testing sample size is 20%. And for consistency of the model results, set “random_state = 0”)(iii) (8 points) Show/output confusion matrix and the accuracy score, precision score, recall score, F1 score. Also provide definition of F1 score. (F1 score was not taught in class, self-research required).(iv) (4 points) Based on results from step (3), provide your interpretation of how the model works (student can either praise the model or criticize the model, and provide your rationale).(v) (4 points) (self-researching required) Using model make prediction: What are the estimated log-odds of graduate school admission for a student with a GPA of 3.2 and a GRE score of 670 who attended a rank 1 school? How about a student who attended a rank 2 school, but who had a GPA of 3.7 and GRE of 750? (Hint: there are many ways to make prediction using logistic model. One way to do it is make the new data the same as the X_test format, and use similar code as the “logistic_regression.predict(X_test)” .Note: for this Part I(b) logistic regression model, no feature scaling is needed.Project Part (c)There are total of 4 options in this section, choose one of them as part c of your project. (If you do multiple of the following options, you will receive the one with the highest score.)(FYI, option 3 and 4 involves coding and self researching, and thus considered harder. Thus they come with extra bonus points.)Option 1: Documentation of Machine Learning model code (17 points)For part c (option 1), you were provided an Exploratory Data Analysis (“EDA”) code that is used to prepare the data/analysis for building models (“Project Part c Machine Learning EDA.ipynb”). Your task is to document the code clearly, including both high level what the code do as well as provide enough details about the code itself. The final goal of the document is aimed to help a Python beginner, who has no or little previous python/coding experience, to understand the logistic regression model code. Do assume the personnel have logistic regression model and other modeling experience, and the help he/she needs from you is purely on the Python code side.Your document will consist of three parts:1.(7 points) In a separate document (recommended in Microsoft Word/pdf), provide insights related to the following topics/section to help the reader grasp the high level picture. Each section listed below will normally require one to two paragraphs of narrative. Feel free to write more whenever you see fit.Overview of the model/codeData Cleaning and Analytics2.(10 points) In supplement to the above document, provide enough details to help the reader to understand the code within the Jupyter Notebook. You will NOT need to provide explanation to each single line of code, BUT enough details should be provided to the key code based on your understanding. Again, the ultimate goal of the document is to help a Python beginner, who has no or little previous python/coding experience, to understand clearly the logistic regression model code.Your notation of the code can come in as either one of the following two formats:As comments using # inside the coding area, for example:[1] This is to ensure that the Python code can be run without error, as the case in daily business world.[2] BTC: Bitcoin; ETH: Ethereum; LTC: Litecoin are three top ranked cryptocurrencies by market caps[3] The five csv files are downloaded using the above links and “Download Data” option. See picture below.Project Part I (total 50 points), has three parts:I(a), 18 pointsI(b), 25 pointsI(c), 17 pointsRefer to blackboard for Due Date information.Important Notes (please read before starting the projects):1.Student is required to finish the project independently. For details, refer to the syllabus.2.All the homework & project are required to be written in python code and submitted in Jupyter Notebook (.ipynb) with Outputs/Results (“Run all” and “save”). If Outputs/Results are not shown, I will try to run the code from my end. If there is an error, any code/comments before the error will be graded. However, any code/comments after the error will be scored started from the basis of zero with the instructor’s best judgment for any merit, and the scores will be final without further negotiation[1].3.The submission can be one consolidated .ipynb (together with other files) or multiple .ipynb(s) as you see fit.4.Do not alter the original input data. Students may not get full points if the original input data is altered outside Python/Jupyter Notebook. For example, if the inputs are provided as five csv files, the requirement is to read in all the input files in Python/Jupyter Notebook as it is. (Partial) Points will be deducted if the five files were processed outside Python/Jupyter Notebook such as Excel before read in the files.5.If you would like to work on project part II for bonus points, you must submit part I as final and send me an email requesting the part II. Students are not allowed to submit part II without submit part I as final.6.The project assignment may require some levels of research effort, including online article/code (main source), research paper as well as textbook research.7.Updates to the project will be published in Blackboard, please pay attention to blackboard announcement. Updates/ Blackboard announcement will be mostly for clarification purpose.8.If there is any question, please email me.undefinedProject Part (a) Data Analysis: Triangular Arbitrage for Cryptocurrency (18 points)Introduction:A cryptocurrency is a digital or virtual currency that uses cryptography for security. The “crypto” in cryptocurrencies refers to complicated cryptography which allows for a particular digital token to be generated, stored, and transacted securely and, typically, anonymously. Alongside this important “crypto” feature of these currencies is a common commitment to decentralization. Many cryptocurrencies are decentralized systems based on “blockchain” technology, a distributed ledger enforced by a disparate network of computers.The first blockchain-based cryptocurrency was “Bitcoin”, which still remains the most popular and most valuable. Today, there are thousands of alternate cryptocurrencies with various functions or specifications. Some of these are clones of Bitcoin while others are forks, or new cryptocurrencies that split off from an already existing one.For more about cryptocurrency, please refer the Investopedia.Triangular arbitrage of cryptocurrency:Consider a collection of three (3) cryptocurrencies {BTC, ETH, LTC[2]} along with USD (US dollars). We would like to test for triangular arbitrage opportunities. Triangular arbitrage opportunities means one should be able to take in any initial capital (in any of the currencies), and return the resulting amount after a cycle (i.e. A->B->C->A). For instance, one can start with a given amount of USD, exchange for BTC, and then ETH, and then back to USD. There are several more combinations (e.g. start with BTC/ETH/LTC instead).At any point in time, we observe the bitcoin price in terms of USD. This price is denoted by BTC-USD. You can find it here BTC to USD. At the same time, one can derive the value of BTC-USD implied by ETH. Here’s how it works: Observe both ETH-USD and ETH-BTC, then their ratio should represent BTC-USD. Here’s a real example: On March 10, 2019, at the same exchange, Brittrex, this is what we observe. As seen below, the derived BTC-USD (computed by division) is different from the observed BTC-USD.ETH-USD135.27derived BTC-USD3915.6487ETH-BTC0.034546observed BTC-USD3915.3000You can find more prices here: ETH to BTC; ETH to USD; BTC to USD Project tasks (i) (6 points) Using the available historical data provided (the included csv files[3], date range: 3/10/2018--3/10/2019), plot the time series (line charts) of the two BTC-USD prices (derived and observed) on the same figure over this period. Note to use the “Price” column in each “csv” data instead of “Open”, “High” “Low” columns. Also note the date column is in reverse order, you may or may not need to sort it.(ii) (6 points) Compute the spread (i.e. difference between the two time series) and plot its time series.Compute the mean and standard deviation during this period (3/10/2018--3/10/2019). Also, show the histogram of the daily spreads to see its distribution.(iii) (6 points) Now repeat parts (i)-(ii) for Litecoin (LTC). That is, replace ETH above with LTC. You’ll need LTC-USD, LTC-BTC, and BTC-USD.Note: you may find the relevant prices using these links: link1, link2, link3(The links are provided for information purpose. All the input data has been provided as csv files)If you think further, you can replace LTC with other coins, and also BTC with another major crypto or Stablecoin. There are truly numerous combinations for triangular arbitrage in the crypto market! (Note: not your task)Hint1: Your output should be similar or identical to the figures in “I(a) Sample Output” folder. Feel free to create your own plot titles and labels as you see fit.Hint2: One of the input column has string dollar amount value similar to “4,000”. You will need to convert to numeric value 4000 before plot charts. You may consider replace or other functions to remove the comma in “4,000”.Project Part (b) Logistic Modeling (25 points)Logistic regression, also called a logit model, is used to model dichotomous outcome variables. In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables. Here we are going to use what we learned from the class (mainly Lecture 05, and also Lecture 06, 07) to help a researcher build a logistic regression model.The researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable. There are three predictor variables: GRE, GPA scores and “rank”. We will treat the variables GRE and GPA as continuous. The variable “rank” takes on the values 1 through 4. Institutions with a rank of 1 have the highest prestige, while those with a rank of 4 have the lowest. Use all the three variables to build logistic model.Using the data “Graduate_School_Admission.csv” provided.Project tasks(i) (3 points) Mimic what we learned in class, provide at least three example codes of Exploratory Data Analysis (“EDA”). “df.describe()” could serve as one example of EDA since it provides the count, mean, standard deviation and other information for the numeric variables. Run the codes and output results.(optional) provide brief interpretation of the EDA results(ii) (6 points) Split the data into training data and testing data. (splitting ratio is 4:1, in other word, testing sample size is 20%. And for consistency of the model results, set “random_state = 0”)(iii) (8 points) Show/output confusion matrix and the accuracy score, precision score, recall score, F1 score. Also provide definition of F1 score. (F1 score was not taught in class, self-research required).(iv) (4 points) Based on results from step (3), provide your interpretation of how the model works (student can either praise the model or criticize the model, and provide your rationale).(v) (4 points) (self-researching required) Using model make prediction: What are the estimated log-odds of graduate school admission for a student with a GPA of 3.2 and a GRE score of 670 who attended a rank 1 school? How about a student who attended a rank 2 school, but who had a GPA of 3.7 and GRE of 750? (Hint: there are many ways to make prediction using logistic model. One way to do it is make the new data the same as the X_test format, and use similar code as the “logistic_regression.predict(X_test)” .Note: for this Part I(b) logistic regression model, no feature scaling is needed.Project Part (c)There are total of 4 options in this section, choose one of them as part c of your project. (If you do multiple of the following options, you will receive the one with the highest score.)(FYI, option 3 and 4 involves coding and self researching, and thus considered harder. Thus they come with extra bonus points.)Option 1: Documentation of Machine Learning model code (17 points)For part c (option 1), you were provided an Exploratory Data Analysis (“EDA”) code that is used to prepare the data/analysis for building models (“Project Part c Machine Learning EDA.ipynb”). Your task is to document the code clearly, including both high level what the code do as well as provide enough details about the code itself. The final goal of the document is aimed to help a Python beginner, who has no or little previous python/coding experience, to understand the logistic regression model code. Do assume the personnel have logistic regression model and other modeling experience, and the help he/she needs from you is purely on the Python code side.Your document will consist of three parts:1.(7 points) In a separate document (recommended in Microsoft Word/pdf), provide insights related to the following topics/section to help the reader grasp the high level picture. Each section listed below will normally require one to two paragraphs of narrative. Feel free to write more whenever you see fit.Overview of the model/codeData Cleaning and Analytics2.(10 points) In supplement to the above document, provide enough details to help the reader to understand the code within the Jupyter Notebook. You will NOT need to provide explanation to each single line of code, BUT enough details should be provided to the key code based on your understanding. Again, the ultimate goal of the document is to help a Python beginner, who has no or little previous python/coding experience, to understand clearly the logistic regression model code.Your notation of the code can come in as either one of the following two formats:As comments using # inside the coding area, for example:[1] This is to ensure that the Python code can be run without error, as the case in daily business world.[2] BTC: Bitcoin; ETH: Ethereum; LTC: Litecoin are three top ranked cryptocurrencies by market caps[3] The five csv files are downloaded using the above links and “Download Data” option. See picture below.I(c), 17 pointsRefer to blackboard for Due Date information.Important Notes (please read before starting the projects):1.Student is required to finish the project independently. For details, refer to the syllabus.2.All the homework & project are required to be written in python code and submitted in Jupyter Notebook (.ipynb) with Outputs/Results (“Run all” and “save”). If Outputs/Results are not shown, I will try to run the code from my end. If there is an error, any code/comments before the error will be graded. However, any code/comments after the error will be scored started from the basis of zero with the instructor’s best judgment for any merit, and the scores will be final without further negotiation[1].3.The submission can be one consolidated .ipynb (together with other files) or multiple .ipynb(s) as you see fit.4.Do not alter the original input data. Students may not get full points if the original input data is altered outside Python/Jupyter Notebook. For example, if the inputs are provided as five csv files, the requirement is to read in all the input files in Python/Jupyter Notebook as it is. (Partial) Points will be deducted if the five files were processed outside Python/Jupyter Notebook such as Excel before read in the files.5.If you would like to work on project part II for bonus points, you must submit part I as final and send me an email requesting the part II. Students are not allowed to submit part II without submit part I as final.6.The project assignment may require some levels of research effort, including online article/code (main source), research paper as well as textbook research.7.Updates to the project will be published in Blackboard, please pay attention to blackboard announcement. Updates/ Blackboard announcement will be mostly for clarification purpose.8.If there is any question, please email me.undefinedProject Part (a) Data Analysis: Triangular Arbitrage for Cryptocurrency (18 points)Introduction:A cryptocurrency is a digital or virtual currency that uses cryptography for security. The “crypto” in cryptocurrencies refers to complicated cryptography which allows for a particular digital token to be generated, stored, and transacted securely and, typically, anonymously. Alongside this important “crypto” feature of these currencies is a common commitment to decentralization. Many cryptocurrencies are decentralized systems based on “blockchain” technology, a distributed ledger enforced by a disparate network of computers.The first blockchain-based cryptocurrency was “Bitcoin”, which still remains the most popular and most valuable. Today, there are thousands of alternate cryptocurrencies with various functions or specifications. Some of these are clones of Bitcoin while others are forks, or new cryptocurrencies that split off from an already existing one.For more about cryptocurrency, please refer the Investopedia.Triangular arbitrage of cryptocurrency:Consider a collection of three (3) cryptocurrencies {BTC, ETH, LTC[2]} along with USD (US dollars). We would like to test for triangular arbitrage opportunities. Triangular arbitrage opportunities means one should be able to take in any initial capital (in any of the currencies), and return the resulting amount after a cycle (i.e. A->B->C->A). For instance, one can start with a given amount of USD, exchange for BTC, and then ETH, and then back to USD. There are several more combinations (e.g. start with BTC/ETH/LTC instead).At any point in time, we observe the bitcoin price in terms of USD. This price is denoted by BTC-USD. You can find it here BTC to USD. At the same time, one can derive the value of BTC-USD implied by ETH. Here’s how it works: Observe both ETH-USD and ETH-BTC, then their ratio should represent BTC-USD. Here’s a real example: On March 10, 2019, at the same exchange, Brittrex, this is what we observe. As seen below, the derived BTC-USD (computed by division) is different from the observed BTC-USD.ETH-USD135.27derived BTC-USD3915.6487ETH-BTC0.034546observed BTC-USD3915.3000You can find more prices here: ETH to BTC; ETH to USD; BTC to USD Project tasks (i) (6 points) Using the available historical data provided (the included csv files[3], date range: 3/10/2018--3/10/2019), plot the time series (line charts) of the two BTC-USD prices (derived and observed) on the same figure over this period. Note to use the “Price” column in each “csv” data instead of “Open”, “High” “Low” columns. Also note the date column is in reverse order, you may or may not need to sort it.(ii) (6 points) Compute the spread (i.e. difference between the two time series) and plot its time series.Compute the mean and standard deviation during this period (3/10/2018--3/10/2019). Also, show the histogram of the daily spreads to see its distribution.(iii) (6 points) Now repeat parts (i)-(ii) for Litecoin (LTC). That is, replace ETH above with LTC. You’ll need LTC-USD, LTC-BTC, and BTC-USD.Note: you may find the relevant prices using these links: link1, link2, link3(The links are provided for information purpose. All the input data has been provided as csv files)If you think further, you can replace LTC with other coins, and also BTC with another major crypto or Stablecoin. There are truly numerous combinations for triangular arbitrage in the crypto market! (Note: not your task)Hint1: Your output should be similar or identical to the figures in “I(a) Sample Output” folder. Feel free to create your own plot titles and labels as you see fit.Hint2: One of the input column has string dollar amount value similar to “4,000”. You will need to convert to numeric value 4000 before plot charts. You may consider replace or other functions to remove the comma in “4,000”.Project Part (b) Logistic Modeling (25 points)Logistic regression, also called a logit model, is used to model dichotomous outcome variables. In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables. Here we are going to use what we learned from the class (mainly Lecture 05, and also Lecture 06, 07) to help a researcher build a logistic regression model.The researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable. There are three predictor variables: GRE, GPA scores and “rank”. We will treat the variables GRE and GPA as continuous. The variable “rank” takes on the values 1 through 4. Institutions with a rank of 1 have the highest prestige, while those with a rank of 4 have the lowest. Use all the three variables to build logistic model.Using the data “Graduate_School_Admission.csv” provided.Project tasks(i) (3 points) Mimic what we learned in class, provide at least three example codes of Exploratory Data Analysis (“EDA”). “df.describe()” could serve as one example of EDA since it provides the count, mean, standard deviation and other information for the numeric variables. Run the codes and output results.(optional) provide brief interpretation of the EDA results(ii) (6 points) Split the data into training data and testing data. (splitting ratio is 4:1, in other word, testing sample size is 20%. And for consistency of the model results, set “random_state = 0”)(iii) (8 points) Show/output confusion matrix and the accuracy score, precision score, recall score, F1 score. Also provide definition of F1 score. (F1 score was not taught in class, self-research required).(iv) (4 points) Based on results from step (3), provide your interpretation of how the model works (student can either praise the model or criticize the model, and provide your rationale).(v) (4 points) (self-researching required) Using model make prediction: What are the estimated log-odds of graduate school admission for a student with a GPA of 3.2 and a GRE score of 670 who attended a rank 1 school? How about a student who attended a rank 2 school, but who had a GPA of 3.7 and GRE of 750? (Hint: there are many ways to make prediction using logistic model. One way to do it is make the new data the same as the X_test format, and use similar code as the “logistic_regression.predict(X_test)” .Note: for this Part I(b) logistic regression model, no feature scaling is needed.Project Part (c)There are total of 4 options in this section, choose one of them as part c of your project. (If you do multiple of the following options, you will receive the one with the highest score.)(FYI, option 3 and 4 involves coding and self researching, and thus considered harder. Thus they come with extra bonus points.)Option 1: Documentation of Machine Learning model code (17 points)For part c (option 1), you were provided an Exploratory Data Analysis (“EDA”) code that is used to prepare the data/analysis for building models (“Project Part c Machine Learning EDA.ipynb”). Your task is to document the code clearly, including both high level what the code do as well as provide enough details about the code itself. The final goal of the document is aimed to help a Python beginner, who has no or little previous python/coding experience, to understand the logistic regression model code. Do assume the personnel have logistic regression model and other modeling experience, and the help he/she needs from you is purely on the Python code side.Your document will consist of three parts:1.(7 points) In a separate document (recommended in Microsoft Word/pdf), provide insights related to the following topics/section to help the reader grasp the high level picture. Each section listed below will normally require one to two paragraphs of narrative. Feel free to write more whenever you see fit.Overview of the model/codeData Cleaning and Analytics2.(10 points) In supplement to the above document, provide enough details to help the reader to understand the code within the Jupyter Notebook. You will NOT need to provide explanation to each single line of code, BUT enough details should be provided to the key code based on your understanding. Again, the ultimate goal of the document is to help a Python beginner, who has no or little previous python/coding experience, to understand clearly the logistic regression model code.Your notation of the code can come in as either one of the following two formats:As comments using # inside the coding area, for example:[1] This is to ensure that the Python code can be run without error, as the case in daily business world.[2] BTC: Bitcoin; ETH: Ethereum; LTC: Litecoin are three top ranked cryptocurrencies by market caps[3] The five csv files are downloaded using the above links and “Download Data” option. See picture below.
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University of the Cumberlands Inferential Statistics in Decision Making Paper
Question 1- A department chair in an academic department wanted to know if there was a difference between spring and fall ...
University of the Cumberlands Inferential Statistics in Decision Making Paper
Question 1- A department chair in an academic department wanted to know if there was a difference between spring and fall enrollment in his department. The department offered the same six courses both semesters. What test should he conduct to determine if there is a significant difference in the enrollment between the semesters? Write up the results and determine what do the results mean? Here is the data set: Spring Fall 30 50 35 28 40 30 45 39 50 40 55 25 Question 2- A researcher wants to know if there is a difference between student's mid-term exam scores and final exam scores in a business course. She has both sets of scores from her spring semester for one section of the class. What test should she conduct? Choose the correct answer. A-Descriptive Statistics B-ANOVA-One Way C-Paired samples t-test D-Independent samples t-test Question 3-What is the correct way to write the following t-test results and interpret the data? Choose the right answer. A B Mean 40.625 61.66667 Variance 504.5535714 462.6667 Observations 8 6 Pooled Variance 487.1006944 Hypothesized Mean Difference 0 df 12 t Stat -1.76533594 P(T<=t) one-tail 0.051458003 t Critical one-tail 1.782287556 P(T<=t) two-tail 0.102916007 t Critical two-tail 2.17881283 A- There is a significant difference between the means of A (M=40.63) and B (M=61.67), (t [12] = 1.76, p < .05). B-There is no significant difference between the means of A (M=40.63) and B (M=61.67), (t [12] = 1.76, p < .05). C-There is no significant difference between the means of A (M=40.63) and B (M=61.67), (t [12] = 1.76, p > .05). D-There is a significant difference between the means of A (M=40.63) and B (M=61.67), (t [12] = 2.18, p > .05). Question 4- A researcher wanted to know if there was a significant difference in cyber security breaches between cities on two different continents. What test should he conduct to determine if there is a significant difference in cyber security breaches between the continents? Write up the results and determine what do the results mean? Here is the data set: Asia Europe 17 20 25 30 26 15 27 14 28 12 50
CU Balance Scorecards Impact Knowledge Creation Culture and Strategy Essay
Information Systems for Business and Beyond Questions:
Chapter 6 – study questions 1-10, Exercise 3, 6, & 7
Informat ...
CU Balance Scorecards Impact Knowledge Creation Culture and Strategy Essay
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Chapter 6 – study questions 1-10, Exercise 3, 6, & 7
Information Technology and Organizational Learning Assignment:
Chapter 6 – Review the section on knowledge creation, culture, and strategy. Explain how balance scorecards impact knowledge creation, culture, and strategy. Why are these important concepts to understand within an organization?
Note the first assignment should be in one section and the second section should have the information from the Information Technology and Organizational Learning assignment. The paper requirements for the two-pages applies to the second part of the assignment directly related to the Information Technology and Organizational Learning assignment.
Assignment 1: Disaster Recovery Plan
Assignment 1: Disaster Recovery PlanDue Week 6 and worth
120 points
You are an administrator for the Contoso Corporatio ...
Assignment 1: Disaster Recovery Plan
Assignment 1: Disaster Recovery PlanDue Week 6 and worth
120 points
You are an administrator for the Contoso Corporation. The Contoso Corporation
has two (2) large sites. The corporate office is in New York and the
manufacturing site is in Cleveland, Ohio. In addition, the Contoso Corporation
has five (5) smaller sites.
At the Corporate site, you currently have eight (8) physical Hyper-V hosts
that host approximately one hundred twenty (120) virtual machines. The virtual
machines are running Windows Server 2008 R2 and Windows Server 2012. The servers
are needed to host email, various databases, file services, print services, and
a point-of sales-application. The virtual machines are stored on a centrally
located storage area network (SAN) that has multiple power supplies, and use
redundant array of independent disks (RAID).
Over the last three (3) years, you have had two (2) power failures that
caused the servers to go down at the corporate office. Recently, during one (1)
of these incidents, files on one (1) of the servers were corrupted. It was later
discovered that the backup could only partially restore the data files, but
could not restore the server itself.
The servers must remain available and reliable. As the administrator, you
must ensure that you can recover from a failure or problem quickly with minimum
loss.
Write a three to five (3-5) page paper in which you:
Develop a continuity plan / disaster recovery plan for Contoso Corporation
in which you address the following:
Suggest an approach that provides redundancy (failover) for all mission
critical functions in the event of a disaster including power failure. Consider
the following in your approach: Essential Infrastructure; Email; Database; File
Services; Print Services; Point-of-Sale Applications; Power Redundancy
Solutions.
Identify the changes that should be made to the backup procedures in order
to address the problem with the server backups.
Outline the necessary actions that Server Administrators must verify for
proper operation of servers in the event of a failover.
Provide a plan for returning all services back to the primary systems once
the crisis has passed.
Examine the pros and cons of moving part of the data center to the Cleveland
office and housing it there on a permanent basis. Provide your recommendation
for the stated action.
Your assignment must follow these formatting requirements:
Be typed, double spaced, using Times New Roman font (size 12), with one-inch
margins on all sides; citations and references must follow APA or
school-specific format. Check with your professor for any additional
instructions.
Include a cover page containing the title of the assignment, the student’s
name, the professor’s name, the course title, and the date. The cover page and
the reference page are not included in the required assignment page
length.
The specific course learning outcomes associated with this assignment
are:
Demonstrate the ability to describe, manage, and configure load balancing
and failover clustering.
Demonstrate the ability to configure and manage backups to include
role-specific and online or cloud environments.
Demonstrate the ability to recover servers to include files and
volumes.
Use technology and information resources to research issues in advanced
network infrastructure environments.
Write clearly and concisely about advanced network infrastructure topics
using proper writing mechanics and technical style conventions.
University of Phoenix Manage Your Health Inc Recreation & Wellness Intranet Worksheet
Assignment ContentA project’s Work Breakdown Structure (WBS) and Gantt chart provide structure for a project. In this we ...
University of Phoenix Manage Your Health Inc Recreation & Wellness Intranet Worksheet
Assignment ContentA project’s Work Breakdown Structure (WBS) and Gantt chart provide structure for a project. In this week’s assignment you develop both the WBS and Gantt chart for a fictitious organization. The Manage Your Health, Inc (MYH) scenario will be used in the weekly assignments for the rest of the course.Review the Manage Your Health Scenario and follow the directions below for completing a WBS and Gantt chart.Develop a work breakdown structure (WBS) for the project. Break down the work to Level 3 or Level 4, as appropriate. Use the Work Breakdown Structure Template and Work Breakdown Structure example in this text as guides. Upload the WBS in list form. The WBS should be based on the information that would be in a project scope of this scenario. You can review your project plan from Wk 1. Create a Gantt chart using the WBS you developed with Microsoft Excel or another software of your choice and approved by your instructor. Do not enter any durations or dependencies. Submit 2 deliverables: the WBS and Gantt Chart.
Assignment 1: Disaster Recovery Plan
Assignment 1: Disaster Recovery PlanDue Week 6 and worth 120 pointsYou are an administrator for the Contoso Corporation. T ...
Assignment 1: Disaster Recovery Plan
Assignment 1: Disaster Recovery PlanDue Week 6 and worth 120 pointsYou are an administrator for the Contoso Corporation. The Contoso Corporation has two (2) large sites. The corporate office is in New York and the manufacturing site is in Cleveland, Ohio. In addition, the Contoso Corporation has five (5) smaller sites. At the Corporate site, you currently have eight (8) physical Hyper-V hosts that host approximately one hundred twenty (120) virtual machines. The virtual machines are running Windows Server 2008 R2 and Windows Server 2012. The servers are needed to host email, various databases, file services, print services, and a point-of sales-application. The virtual machines are stored on a centrally located storage area network (SAN) that has multiple power supplies, and use redundant array of independent disks (RAID).Over the last three (3) years, you have had two (2) power failures that caused the servers to go down at the corporate office. Recently, during one (1) of these incidents, files on one (1) of the servers were corrupted. It was later discovered that the backup could only partially restore the data files, but could not restore the server itself.The servers must remain available and reliable. As the administrator, you must ensure that you can recover from a failure or problem quickly with minimum loss. Write a three to five (3-5) page paper in which you:Develop a continuity plan / disaster recovery plan for Contoso Corporation in which you address the following:Suggest an approach that provides redundancy (failover) for all mission critical functions in the event of a disaster including power failure. Consider the following in your approach: Essential Infrastructure; Email; Database; File Services; Print Services; Point-of-Sale Applications; Power Redundancy Solutions.Identify the changes that should be made to the backup procedures in order to address the problem with the server backups.Outline the necessary actions that Server Administrators must verify for proper operation of servers in the event of a failover.Provide a plan for returning all services back to the primary systems once the crisis has passed.Examine the pros and cons of moving part of the data center to the Cleveland office and housing it there on a permanent basis. Provide your recommendation for the stated action. Your assignment must follow these formatting requirements:Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.The specific course learning outcomes associated with this assignment are:Demonstrate the ability to describe, manage, and configure load balancing and failover clustering.Demonstrate the ability to configure and manage backups to include role-specific and online or cloud environments.Demonstrate the ability to recover servers to include files and volumes.Use technology and information resources to research issues in advanced network infrastructure environments.Write clearly and concisely about advanced network infrastructure topics using proper writing mechanics and technical style conventions.
Complete python project
Project Part I (total 50 points), has three parts:I(a), 18 pointsI(b), 25 pointsProject Part I (total 50 points), has thre ...
Complete python project
Project Part I (total 50 points), has three parts:I(a), 18 pointsI(b), 25 pointsProject Part I (total 50 points), has three parts:I(a), 18 pointsI(b), 25 pointsI(c), 17 pointsRefer to blackboard for Due Date information.Important Notes (please read before starting the projects):1.Student is required to finish the project independently. For details, refer to the syllabus.2.All the homework & project are required to be written in python code and submitted in Jupyter Notebook (.ipynb) with Outputs/Results (“Run all” and “save”). If Outputs/Results are not shown, I will try to run the code from my end. If there is an error, any code/comments before the error will be graded. However, any code/comments after the error will be scored started from the basis of zero with the instructor’s best judgment for any merit, and the scores will be final without further negotiation[1].3.The submission can be one consolidated .ipynb (together with other files) or multiple .ipynb(s) as you see fit.4.Do not alter the original input data. Students may not get full points if the original input data is altered outside Python/Jupyter Notebook. For example, if the inputs are provided as five csv files, the requirement is to read in all the input files in Python/Jupyter Notebook as it is. (Partial) Points will be deducted if the five files were processed outside Python/Jupyter Notebook such as Excel before read in the files.5.If you would like to work on project part II for bonus points, you must submit part I as final and send me an email requesting the part II. Students are not allowed to submit part II without submit part I as final.6.The project assignment may require some levels of research effort, including online article/code (main source), research paper as well as textbook research.7.Updates to the project will be published in Blackboard, please pay attention to blackboard announcement. Updates/ Blackboard announcement will be mostly for clarification purpose.8.If there is any question, please email me.undefinedProject Part (a) Data Analysis: Triangular Arbitrage for Cryptocurrency (18 points)Introduction:A cryptocurrency is a digital or virtual currency that uses cryptography for security. The “crypto” in cryptocurrencies refers to complicated cryptography which allows for a particular digital token to be generated, stored, and transacted securely and, typically, anonymously. Alongside this important “crypto” feature of these currencies is a common commitment to decentralization. Many cryptocurrencies are decentralized systems based on “blockchain” technology, a distributed ledger enforced by a disparate network of computers.The first blockchain-based cryptocurrency was “Bitcoin”, which still remains the most popular and most valuable. Today, there are thousands of alternate cryptocurrencies with various functions or specifications. Some of these are clones of Bitcoin while others are forks, or new cryptocurrencies that split off from an already existing one.For more about cryptocurrency, please refer the Investopedia.Triangular arbitrage of cryptocurrency:Consider a collection of three (3) cryptocurrencies {BTC, ETH, LTC[2]} along with USD (US dollars). We would like to test for triangular arbitrage opportunities. Triangular arbitrage opportunities means one should be able to take in any initial capital (in any of the currencies), and return the resulting amount after a cycle (i.e. A->B->C->A). For instance, one can start with a given amount of USD, exchange for BTC, and then ETH, and then back to USD. There are several more combinations (e.g. start with BTC/ETH/LTC instead).At any point in time, we observe the bitcoin price in terms of USD. This price is denoted by BTC-USD. You can find it here BTC to USD. At the same time, one can derive the value of BTC-USD implied by ETH. Here’s how it works: Observe both ETH-USD and ETH-BTC, then their ratio should represent BTC-USD. Here’s a real example: On March 10, 2019, at the same exchange, Brittrex, this is what we observe. As seen below, the derived BTC-USD (computed by division) is different from the observed BTC-USD.ETH-USD135.27derived BTC-USD3915.6487ETH-BTC0.034546observed BTC-USD3915.3000You can find more prices here: ETH to BTC; ETH to USD; BTC to USD Project tasks (i) (6 points) Using the available historical data provided (the included csv files[3], date range: 3/10/2018--3/10/2019), plot the time series (line charts) of the two BTC-USD prices (derived and observed) on the same figure over this period. Note to use the “Price” column in each “csv” data instead of “Open”, “High” “Low” columns. Also note the date column is in reverse order, you may or may not need to sort it.(ii) (6 points) Compute the spread (i.e. difference between the two time series) and plot its time series.Compute the mean and standard deviation during this period (3/10/2018--3/10/2019). Also, show the histogram of the daily spreads to see its distribution.(iii) (6 points) Now repeat parts (i)-(ii) for Litecoin (LTC). That is, replace ETH above with LTC. You’ll need LTC-USD, LTC-BTC, and BTC-USD.Note: you may find the relevant prices using these links: link1, link2, link3(The links are provided for information purpose. All the input data has been provided as csv files)If you think further, you can replace LTC with other coins, and also BTC with another major crypto or Stablecoin. There are truly numerous combinations for triangular arbitrage in the crypto market! (Note: not your task)Hint1: Your output should be similar or identical to the figures in “I(a) Sample Output” folder. Feel free to create your own plot titles and labels as you see fit.Hint2: One of the input column has string dollar amount value similar to “4,000”. You will need to convert to numeric value 4000 before plot charts. You may consider replace or other functions to remove the comma in “4,000”.Project Part (b) Logistic Modeling (25 points)Logistic regression, also called a logit model, is used to model dichotomous outcome variables. In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables. Here we are going to use what we learned from the class (mainly Lecture 05, and also Lecture 06, 07) to help a researcher build a logistic regression model.The researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable. There are three predictor variables: GRE, GPA scores and “rank”. We will treat the variables GRE and GPA as continuous. The variable “rank” takes on the values 1 through 4. Institutions with a rank of 1 have the highest prestige, while those with a rank of 4 have the lowest. Use all the three variables to build logistic model.Using the data “Graduate_School_Admission.csv” provided.Project tasks(i) (3 points) Mimic what we learned in class, provide at least three example codes of Exploratory Data Analysis (“EDA”). “df.describe()” could serve as one example of EDA since it provides the count, mean, standard deviation and other information for the numeric variables. Run the codes and output results.(optional) provide brief interpretation of the EDA results(ii) (6 points) Split the data into training data and testing data. (splitting ratio is 4:1, in other word, testing sample size is 20%. And for consistency of the model results, set “random_state = 0”)(iii) (8 points) Show/output confusion matrix and the accuracy score, precision score, recall score, F1 score. Also provide definition of F1 score. (F1 score was not taught in class, self-research required).(iv) (4 points) Based on results from step (3), provide your interpretation of how the model works (student can either praise the model or criticize the model, and provide your rationale).(v) (4 points) (self-researching required) Using model make prediction: What are the estimated log-odds of graduate school admission for a student with a GPA of 3.2 and a GRE score of 670 who attended a rank 1 school? How about a student who attended a rank 2 school, but who had a GPA of 3.7 and GRE of 750? (Hint: there are many ways to make prediction using logistic model. One way to do it is make the new data the same as the X_test format, and use similar code as the “logistic_regression.predict(X_test)” .Note: for this Part I(b) logistic regression model, no feature scaling is needed.Project Part (c)There are total of 4 options in this section, choose one of them as part c of your project. (If you do multiple of the following options, you will receive the one with the highest score.)(FYI, option 3 and 4 involves coding and self researching, and thus considered harder. Thus they come with extra bonus points.)Option 1: Documentation of Machine Learning model code (17 points)For part c (option 1), you were provided an Exploratory Data Analysis (“EDA”) code that is used to prepare the data/analysis for building models (“Project Part c Machine Learning EDA.ipynb”). Your task is to document the code clearly, including both high level what the code do as well as provide enough details about the code itself. The final goal of the document is aimed to help a Python beginner, who has no or little previous python/coding experience, to understand the logistic regression model code. Do assume the personnel have logistic regression model and other modeling experience, and the help he/she needs from you is purely on the Python code side.Your document will consist of three parts:1.(7 points) In a separate document (recommended in Microsoft Word/pdf), provide insights related to the following topics/section to help the reader grasp the high level picture. Each section listed below will normally require one to two paragraphs of narrative. Feel free to write more whenever you see fit.Overview of the model/codeData Cleaning and Analytics2.(10 points) In supplement to the above document, provide enough details to help the reader to understand the code within the Jupyter Notebook. You will NOT need to provide explanation to each single line of code, BUT enough details should be provided to the key code based on your understanding. Again, the ultimate goal of the document is to help a Python beginner, who has no or little previous python/coding experience, to understand clearly the logistic regression model code.Your notation of the code can come in as either one of the following two formats:As comments using # inside the coding area, for example:[1] This is to ensure that the Python code can be run without error, as the case in daily business world.[2] BTC: Bitcoin; ETH: Ethereum; LTC: Litecoin are three top ranked cryptocurrencies by market caps[3] The five csv files are downloaded using the above links and “Download Data” option. See picture below.Project Part I (total 50 points), has three parts:I(a), 18 pointsI(b), 25 pointsI(c), 17 pointsRefer to blackboard for Due Date information.Important Notes (please read before starting the projects):1.Student is required to finish the project independently. For details, refer to the syllabus.2.All the homework & project are required to be written in python code and submitted in Jupyter Notebook (.ipynb) with Outputs/Results (“Run all” and “save”). If Outputs/Results are not shown, I will try to run the code from my end. If there is an error, any code/comments before the error will be graded. However, any code/comments after the error will be scored started from the basis of zero with the instructor’s best judgment for any merit, and the scores will be final without further negotiation[1].3.The submission can be one consolidated .ipynb (together with other files) or multiple .ipynb(s) as you see fit.4.Do not alter the original input data. Students may not get full points if the original input data is altered outside Python/Jupyter Notebook. For example, if the inputs are provided as five csv files, the requirement is to read in all the input files in Python/Jupyter Notebook as it is. (Partial) Points will be deducted if the five files were processed outside Python/Jupyter Notebook such as Excel before read in the files.5.If you would like to work on project part II for bonus points, you must submit part I as final and send me an email requesting the part II. Students are not allowed to submit part II without submit part I as final.6.The project assignment may require some levels of research effort, including online article/code (main source), research paper as well as textbook research.7.Updates to the project will be published in Blackboard, please pay attention to blackboard announcement. Updates/ Blackboard announcement will be mostly for clarification purpose.8.If there is any question, please email me.undefinedProject Part (a) Data Analysis: Triangular Arbitrage for Cryptocurrency (18 points)Introduction:A cryptocurrency is a digital or virtual currency that uses cryptography for security. The “crypto” in cryptocurrencies refers to complicated cryptography which allows for a particular digital token to be generated, stored, and transacted securely and, typically, anonymously. Alongside this important “crypto” feature of these currencies is a common commitment to decentralization. Many cryptocurrencies are decentralized systems based on “blockchain” technology, a distributed ledger enforced by a disparate network of computers.The first blockchain-based cryptocurrency was “Bitcoin”, which still remains the most popular and most valuable. Today, there are thousands of alternate cryptocurrencies with various functions or specifications. Some of these are clones of Bitcoin while others are forks, or new cryptocurrencies that split off from an already existing one.For more about cryptocurrency, please refer the Investopedia.Triangular arbitrage of cryptocurrency:Consider a collection of three (3) cryptocurrencies {BTC, ETH, LTC[2]} along with USD (US dollars). We would like to test for triangular arbitrage opportunities. Triangular arbitrage opportunities means one should be able to take in any initial capital (in any of the currencies), and return the resulting amount after a cycle (i.e. A->B->C->A). For instance, one can start with a given amount of USD, exchange for BTC, and then ETH, and then back to USD. There are several more combinations (e.g. start with BTC/ETH/LTC instead).At any point in time, we observe the bitcoin price in terms of USD. This price is denoted by BTC-USD. You can find it here BTC to USD. At the same time, one can derive the value of BTC-USD implied by ETH. Here’s how it works: Observe both ETH-USD and ETH-BTC, then their ratio should represent BTC-USD. Here’s a real example: On March 10, 2019, at the same exchange, Brittrex, this is what we observe. As seen below, the derived BTC-USD (computed by division) is different from the observed BTC-USD.ETH-USD135.27derived BTC-USD3915.6487ETH-BTC0.034546observed BTC-USD3915.3000You can find more prices here: ETH to BTC; ETH to USD; BTC to USD Project tasks (i) (6 points) Using the available historical data provided (the included csv files[3], date range: 3/10/2018--3/10/2019), plot the time series (line charts) of the two BTC-USD prices (derived and observed) on the same figure over this period. Note to use the “Price” column in each “csv” data instead of “Open”, “High” “Low” columns. Also note the date column is in reverse order, you may or may not need to sort it.(ii) (6 points) Compute the spread (i.e. difference between the two time series) and plot its time series.Compute the mean and standard deviation during this period (3/10/2018--3/10/2019). Also, show the histogram of the daily spreads to see its distribution.(iii) (6 points) Now repeat parts (i)-(ii) for Litecoin (LTC). That is, replace ETH above with LTC. You’ll need LTC-USD, LTC-BTC, and BTC-USD.Note: you may find the relevant prices using these links: link1, link2, link3(The links are provided for information purpose. All the input data has been provided as csv files)If you think further, you can replace LTC with other coins, and also BTC with another major crypto or Stablecoin. There are truly numerous combinations for triangular arbitrage in the crypto market! (Note: not your task)Hint1: Your output should be similar or identical to the figures in “I(a) Sample Output” folder. Feel free to create your own plot titles and labels as you see fit.Hint2: One of the input column has string dollar amount value similar to “4,000”. You will need to convert to numeric value 4000 before plot charts. You may consider replace or other functions to remove the comma in “4,000”.Project Part (b) Logistic Modeling (25 points)Logistic regression, also called a logit model, is used to model dichotomous outcome variables. In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables. Here we are going to use what we learned from the class (mainly Lecture 05, and also Lecture 06, 07) to help a researcher build a logistic regression model.The researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable. There are three predictor variables: GRE, GPA scores and “rank”. We will treat the variables GRE and GPA as continuous. The variable “rank” takes on the values 1 through 4. Institutions with a rank of 1 have the highest prestige, while those with a rank of 4 have the lowest. Use all the three variables to build logistic model.Using the data “Graduate_School_Admission.csv” provided.Project tasks(i) (3 points) Mimic what we learned in class, provide at least three example codes of Exploratory Data Analysis (“EDA”). “df.describe()” could serve as one example of EDA since it provides the count, mean, standard deviation and other information for the numeric variables. Run the codes and output results.(optional) provide brief interpretation of the EDA results(ii) (6 points) Split the data into training data and testing data. (splitting ratio is 4:1, in other word, testing sample size is 20%. And for consistency of the model results, set “random_state = 0”)(iii) (8 points) Show/output confusion matrix and the accuracy score, precision score, recall score, F1 score. Also provide definition of F1 score. (F1 score was not taught in class, self-research required).(iv) (4 points) Based on results from step (3), provide your interpretation of how the model works (student can either praise the model or criticize the model, and provide your rationale).(v) (4 points) (self-researching required) Using model make prediction: What are the estimated log-odds of graduate school admission for a student with a GPA of 3.2 and a GRE score of 670 who attended a rank 1 school? How about a student who attended a rank 2 school, but who had a GPA of 3.7 and GRE of 750? (Hint: there are many ways to make prediction using logistic model. One way to do it is make the new data the same as the X_test format, and use similar code as the “logistic_regression.predict(X_test)” .Note: for this Part I(b) logistic regression model, no feature scaling is needed.Project Part (c)There are total of 4 options in this section, choose one of them as part c of your project. (If you do multiple of the following options, you will receive the one with the highest score.)(FYI, option 3 and 4 involves coding and self researching, and thus considered harder. Thus they come with extra bonus points.)Option 1: Documentation of Machine Learning model code (17 points)For part c (option 1), you were provided an Exploratory Data Analysis (“EDA”) code that is used to prepare the data/analysis for building models (“Project Part c Machine Learning EDA.ipynb”). Your task is to document the code clearly, including both high level what the code do as well as provide enough details about the code itself. The final goal of the document is aimed to help a Python beginner, who has no or little previous python/coding experience, to understand the logistic regression model code. Do assume the personnel have logistic regression model and other modeling experience, and the help he/she needs from you is purely on the Python code side.Your document will consist of three parts:1.(7 points) In a separate document (recommended in Microsoft Word/pdf), provide insights related to the following topics/section to help the reader grasp the high level picture. Each section listed below will normally require one to two paragraphs of narrative. Feel free to write more whenever you see fit.Overview of the model/codeData Cleaning and Analytics2.(10 points) In supplement to the above document, provide enough details to help the reader to understand the code within the Jupyter Notebook. You will NOT need to provide explanation to each single line of code, BUT enough details should be provided to the key code based on your understanding. Again, the ultimate goal of the document is to help a Python beginner, who has no or little previous python/coding experience, to understand clearly the logistic regression model code.Your notation of the code can come in as either one of the following two formats:As comments using # inside the coding area, for example:[1] This is to ensure that the Python code can be run without error, as the case in daily business world.[2] BTC: Bitcoin; ETH: Ethereum; LTC: Litecoin are three top ranked cryptocurrencies by market caps[3] The five csv files are downloaded using the above links and “Download Data” option. See picture below.I(c), 17 pointsRefer to blackboard for Due Date information.Important Notes (please read before starting the projects):1.Student is required to finish the project independently. For details, refer to the syllabus.2.All the homework & project are required to be written in python code and submitted in Jupyter Notebook (.ipynb) with Outputs/Results (“Run all” and “save”). If Outputs/Results are not shown, I will try to run the code from my end. If there is an error, any code/comments before the error will be graded. However, any code/comments after the error will be scored started from the basis of zero with the instructor’s best judgment for any merit, and the scores will be final without further negotiation[1].3.The submission can be one consolidated .ipynb (together with other files) or multiple .ipynb(s) as you see fit.4.Do not alter the original input data. Students may not get full points if the original input data is altered outside Python/Jupyter Notebook. For example, if the inputs are provided as five csv files, the requirement is to read in all the input files in Python/Jupyter Notebook as it is. (Partial) Points will be deducted if the five files were processed outside Python/Jupyter Notebook such as Excel before read in the files.5.If you would like to work on project part II for bonus points, you must submit part I as final and send me an email requesting the part II. Students are not allowed to submit part II without submit part I as final.6.The project assignment may require some levels of research effort, including online article/code (main source), research paper as well as textbook research.7.Updates to the project will be published in Blackboard, please pay attention to blackboard announcement. Updates/ Blackboard announcement will be mostly for clarification purpose.8.If there is any question, please email me.undefinedProject Part (a) Data Analysis: Triangular Arbitrage for Cryptocurrency (18 points)Introduction:A cryptocurrency is a digital or virtual currency that uses cryptography for security. The “crypto” in cryptocurrencies refers to complicated cryptography which allows for a particular digital token to be generated, stored, and transacted securely and, typically, anonymously. Alongside this important “crypto” feature of these currencies is a common commitment to decentralization. Many cryptocurrencies are decentralized systems based on “blockchain” technology, a distributed ledger enforced by a disparate network of computers.The first blockchain-based cryptocurrency was “Bitcoin”, which still remains the most popular and most valuable. Today, there are thousands of alternate cryptocurrencies with various functions or specifications. Some of these are clones of Bitcoin while others are forks, or new cryptocurrencies that split off from an already existing one.For more about cryptocurrency, please refer the Investopedia.Triangular arbitrage of cryptocurrency:Consider a collection of three (3) cryptocurrencies {BTC, ETH, LTC[2]} along with USD (US dollars). We would like to test for triangular arbitrage opportunities. Triangular arbitrage opportunities means one should be able to take in any initial capital (in any of the currencies), and return the resulting amount after a cycle (i.e. A->B->C->A). For instance, one can start with a given amount of USD, exchange for BTC, and then ETH, and then back to USD. There are several more combinations (e.g. start with BTC/ETH/LTC instead).At any point in time, we observe the bitcoin price in terms of USD. This price is denoted by BTC-USD. You can find it here BTC to USD. At the same time, one can derive the value of BTC-USD implied by ETH. Here’s how it works: Observe both ETH-USD and ETH-BTC, then their ratio should represent BTC-USD. Here’s a real example: On March 10, 2019, at the same exchange, Brittrex, this is what we observe. As seen below, the derived BTC-USD (computed by division) is different from the observed BTC-USD.ETH-USD135.27derived BTC-USD3915.6487ETH-BTC0.034546observed BTC-USD3915.3000You can find more prices here: ETH to BTC; ETH to USD; BTC to USD Project tasks (i) (6 points) Using the available historical data provided (the included csv files[3], date range: 3/10/2018--3/10/2019), plot the time series (line charts) of the two BTC-USD prices (derived and observed) on the same figure over this period. Note to use the “Price” column in each “csv” data instead of “Open”, “High” “Low” columns. Also note the date column is in reverse order, you may or may not need to sort it.(ii) (6 points) Compute the spread (i.e. difference between the two time series) and plot its time series.Compute the mean and standard deviation during this period (3/10/2018--3/10/2019). Also, show the histogram of the daily spreads to see its distribution.(iii) (6 points) Now repeat parts (i)-(ii) for Litecoin (LTC). That is, replace ETH above with LTC. You’ll need LTC-USD, LTC-BTC, and BTC-USD.Note: you may find the relevant prices using these links: link1, link2, link3(The links are provided for information purpose. All the input data has been provided as csv files)If you think further, you can replace LTC with other coins, and also BTC with another major crypto or Stablecoin. There are truly numerous combinations for triangular arbitrage in the crypto market! (Note: not your task)Hint1: Your output should be similar or identical to the figures in “I(a) Sample Output” folder. Feel free to create your own plot titles and labels as you see fit.Hint2: One of the input column has string dollar amount value similar to “4,000”. You will need to convert to numeric value 4000 before plot charts. You may consider replace or other functions to remove the comma in “4,000”.Project Part (b) Logistic Modeling (25 points)Logistic regression, also called a logit model, is used to model dichotomous outcome variables. In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables. Here we are going to use what we learned from the class (mainly Lecture 05, and also Lecture 06, 07) to help a researcher build a logistic regression model.The researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable. There are three predictor variables: GRE, GPA scores and “rank”. We will treat the variables GRE and GPA as continuous. The variable “rank” takes on the values 1 through 4. Institutions with a rank of 1 have the highest prestige, while those with a rank of 4 have the lowest. Use all the three variables to build logistic model.Using the data “Graduate_School_Admission.csv” provided.Project tasks(i) (3 points) Mimic what we learned in class, provide at least three example codes of Exploratory Data Analysis (“EDA”). “df.describe()” could serve as one example of EDA since it provides the count, mean, standard deviation and other information for the numeric variables. Run the codes and output results.(optional) provide brief interpretation of the EDA results(ii) (6 points) Split the data into training data and testing data. (splitting ratio is 4:1, in other word, testing sample size is 20%. And for consistency of the model results, set “random_state = 0”)(iii) (8 points) Show/output confusion matrix and the accuracy score, precision score, recall score, F1 score. Also provide definition of F1 score. (F1 score was not taught in class, self-research required).(iv) (4 points) Based on results from step (3), provide your interpretation of how the model works (student can either praise the model or criticize the model, and provide your rationale).(v) (4 points) (self-researching required) Using model make prediction: What are the estimated log-odds of graduate school admission for a student with a GPA of 3.2 and a GRE score of 670 who attended a rank 1 school? How about a student who attended a rank 2 school, but who had a GPA of 3.7 and GRE of 750? (Hint: there are many ways to make prediction using logistic model. One way to do it is make the new data the same as the X_test format, and use similar code as the “logistic_regression.predict(X_test)” .Note: for this Part I(b) logistic regression model, no feature scaling is needed.Project Part (c)There are total of 4 options in this section, choose one of them as part c of your project. (If you do multiple of the following options, you will receive the one with the highest score.)(FYI, option 3 and 4 involves coding and self researching, and thus considered harder. Thus they come with extra bonus points.)Option 1: Documentation of Machine Learning model code (17 points)For part c (option 1), you were provided an Exploratory Data Analysis (“EDA”) code that is used to prepare the data/analysis for building models (“Project Part c Machine Learning EDA.ipynb”). Your task is to document the code clearly, including both high level what the code do as well as provide enough details about the code itself. The final goal of the document is aimed to help a Python beginner, who has no or little previous python/coding experience, to understand the logistic regression model code. Do assume the personnel have logistic regression model and other modeling experience, and the help he/she needs from you is purely on the Python code side.Your document will consist of three parts:1.(7 points) In a separate document (recommended in Microsoft Word/pdf), provide insights related to the following topics/section to help the reader grasp the high level picture. Each section listed below will normally require one to two paragraphs of narrative. Feel free to write more whenever you see fit.Overview of the model/codeData Cleaning and Analytics2.(10 points) In supplement to the above document, provide enough details to help the reader to understand the code within the Jupyter Notebook. You will NOT need to provide explanation to each single line of code, BUT enough details should be provided to the key code based on your understanding. Again, the ultimate goal of the document is to help a Python beginner, who has no or little previous python/coding experience, to understand clearly the logistic regression model code.Your notation of the code can come in as either one of the following two formats:As comments using # inside the coding area, for example:[1] This is to ensure that the Python code can be run without error, as the case in daily business world.[2] BTC: Bitcoin; ETH: Ethereum; LTC: Litecoin are three top ranked cryptocurrencies by market caps[3] The five csv files are downloaded using the above links and “Download Data” option. See picture below.
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