In this project, you will research and obtain a dataset relevant to a public or private organization or a specific problem domain, and will apply analytics techniques covered in the course to yield actionable insights.Questions/Challenges: As online retail continues to grow in popularity, understanding consumer buying activity online is crucial for making effective business decisions and shaping business strategies. By answering questions such as what are the most profitable items, what customers are most likely to buy, who is most likely to respond to specific strategies, how regularly does a customer purchase x item, how much are customers willing to spend in a single trip/single activity, targeting international audiences, we are able suggest strategies that could optimize the store strategies for operations and money for ads.You will be required to submit a 1-page project proposal (due by October 29), a 2-page project outline (due by November 16), deliver a 5-8 minute presentation in class (November 30), and submit a final written report of their work (due by December 7), as detailed below.Projects may incorporate descriptive, predictive, or prescriptive analytics techniques, or any combination thereof. However, purely descriptive analyses are discouraged; there must be a clear explanation of how the results of the project will influence decision making (either an individual decision or a specific type of decision). That is, groups are required to state clearly and specifically how their results will help the people or organization(s) involved.There is no a priori restriction on the setting or domain of the project. Groups are encouraged to choose topics that are of interest to them. If in doubt about whether a topic would make an appropriate project, ask! The quality of the project depends on many factors, including but not limited to: collection of a sufficient quantity of usable data, appropriate application of the tools and methods learned in class, generation of actionable insights, and clear communication of the group’s work both orally and in writing.The dataset is required to have at least 500 usable data points at a minimum (more is preferable). Smaller datasets are unlikely to yield fruitful results from several methods covered in the course.Project Outline (3%): Due November 16 before class via BlackboardThe project outline should be a Word document no longer than two pages that describes the data set and next steps. It must include:A description of the data set (source, number of data points, variables, etc.)What kinds of analytics techniques the group plans to applyWhat kinds of results or insights the group expects/hopes that the analysis will revealPresentation (6%): November 30, in class.A copy of the slides is due by 3:30 PM Eastern via Blackboard.Presentations should be 5-8 minutes in length. No handouts nor supporting files other than a single .ppt file are permitted. It will be graded both on the correctness/appropriateness of the content, and clarity with which it is presented.The presentation should include:A brief description of the organization or problem domain, and the issue being analyzedA description & snapshot of the data setAn explanation of any unusual data or modeling challenges that the group encounteredAn explanation of the methods used to analyze the data setAn explanation of the results and their implications for the organization or decision maker(s)Written Report (14%): Due December 7 by 5:30 PM Eastern via BlackboardThe written report should be considered a complete report of the work done by the group. It will be graded based on the correctness and thoroughness of the analysis, as well as the clarity with which it is explained. It should include all of the items listed above for the presentation, incorporating any additional supporting material or detailed explanations that were left out of the presentation. It should also incorporate any changes made since the presentation was delivered. Reports of more than 12 pages, inclusive of tables and charts, are discouraged. The dataset should not be included in its entirety; a snapshot of it is sufficient.You are encouraged to work on a draft of the written report in parallel with the presentation; this saves time and tends to result in higher quality reports.Recommended format & structure for the written report:Executive Summary (~1 page): a brief summary of the topic, the dataset, the methods used, and the group’s findings.This part of the report should be targeted toward a supervisor or client; a non-technical reader should be able to understand it.Introduction: an explanation of the topic of the project, including the organization(s) involved, and the issue or challenge the analysis will address. This section should be less than two pages.Data: an explanation of the dataset(s) used by the group, including any cleaning and formatting work that was necessary.Analysis: presentation and discussion of the analytics methods used by the group, including all relevant results and their interpretations.Discussion and Conclusion: a wrap-up of the analysis, explaining the insights and important takeaways from the group’s work.Appendix (if necessary): additional tables, charts, or other pieces of information from the analysis that might be of interest to a reader, but are not germane to the group’s main results. Snippets of R code can be included here to highlight anything unusual or of particular interest, but please do not include R scripts in their entirety. The Appendix does not count toward the 12-page limit.Use double spacing, 11-point Times New Roman font, and 1-inch margins. Be sure to reference any external sources used for quotes or information using a common style guide such as APA (http://www.apastyle.org/manual/index.aspx).The written report must look professional. Grammatical mistakes, typos, inconsistent formatting, etc., will result in points being deducted.Please find below a copy of a draft proposal which should be improved upon:Retail - Consumer ActivityQuestions/Challenges: As online retail continues to grow in popularity, understanding consumer buying activity online is crucial for making effective business decisions and shaping business strategies. By answering questions such as what are the most profitable items, what customers are most likely to buy, who is most likely to respond to specific strategies, how regularly does a customer purchase x item, how much are customers willing to spend in a single trip/single activity, targeting international audiences, we are able suggest strategies that could optimize the store strategies for operations and money for ads. Dataset Description: Invoice of online purchases from a home goods store that tracks who (CustomerID) purchased a specific item (StockCode), the price of the items (UnitPrice) and what items a customer bought in a single “trip” (InvoiceNo).Dataset:Source: Kaggle - OnlineRetail.csvNumber of Data Points: 542KVariables: InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, CountryAnalytics Techniques:AssociationClusteringHierarchical RegressionForecastingActivityThe impact of online shopping on block retail storesWhat items customers are likely to purchase togetherWhat country would respond to targeted ads for online shoppingThe ideal price point per item to encourage customers to buyWhat kinds of results or insights the group expects/hopes that the analysis will reveal:

OverviewFor this assignment, we will practice using inheritance. The exercise will create a class for Major that can be extended for a variety of different college majors, which will be subclasses extended from Major. At least one of the subclasses will extend another subclass. The goal is to write as little code as possible in the subclasses by using inheritance.SpecificationsThe project will create a class called Major. The Major class will be the base class for the variety of different major options available. The tester class, containing the main method, should be used to print the information stored and generated by the subclasses. The Major class should have at least 3 attributes for the base class. Attributes can include such things as major name, minimum GPA or other information a student would want to know about a potential major. You have the flexibility to add as many attributes as you determine appropriate, as long as there are a minimum of 3 attributes. The program should have at least 3 different majors with one of the subclasses extending another subclass. For example, we might have a Computer Science class and a Math classes that extend Major. They will be concrete classes that inherit the public methods from Major, including the getter and setter methods for the private instance variables in Major. We might then have a Math Education class which extends Math and inherits from the Math class, including the elements inherited from Major.The subclasses should use the private instance variables in Major as much as possible rather than creating duplicate instance variables in the subclasses as appropriate. HINT: Remember, public getter and setter methods in Major will be inherited in the extended classes but the private instance variables will not. Instance variables must be private to ensure encapsulation. The extended class should only have instance variables for the attributes not stored in the super() class. At least 2 of the 3 extended classes should have a minimum of two additional instance variables and one additional method not found in Major and unique to the subclass. Also, the extended classes should override the default toString() method to print specific information about the object using most of the instance variables available in the subclass.The output for this project should include the information stored in both the instance variables in the super() class (Major) as well as any instance variables in the subclasses including the overridden toString() method.Expected Output:The expected output should contain the contents of the instance variables for Major and the extended classes. The output should also contain the result of the use of the overridden toString() method. The output should contain include all of the data stored in the instance variables in a way that makes sense to the user.Java RequirementsThe program mustHave a class for Major which is the base class for the inheritance.The program should include a tester class used to call the object methods based on objects created from the appropriate object classes.The extended classes should override the toString() method as appropriate.All of the instance variables should be private and the extended classes should not have duplicate instance variables for those found in the inheritance chain.The main method should use polymorphism to store either an ArrayList or an array of majors. There should be at least one object for each subclass created.Once you have it working as expected upload the .java file to this assignment drop box. You must also submit a Word doc or text file containing the Java code. Assignments not containing both the Java code and the text or Word file will be assigned a 0 for a grade.RubricProject 4 RubricProject 4 RubricCriteriaRatingsPtsThis criterion is linked to a Learning OutcomeMajor class created correctly10 ptsFull MarksMajor class is a class that as at least 3 attributes which are private for the base class and there are appropriate getter/setter methods5 ptsPartial CreditMajor class does not have at least 3 attributes for the base class or the instance variables are public or the getter/setter methods are missing.0 ptsNo MarksMajor class does not have at least 3 attributes for the base classes and the instance variables are either public or there are not getter/setter methods for the instance variables.10 ptsThis criterion is linked to a Learning OutcomeTester class created correctly10 ptsFull MarksTester class is used to access and print all data stored and generated by the subclasses5 ptsPartial CreditTester class is used to access and print some of the data stored and generated by the subclasses0 ptsNo MarksTester class is not used to access and print data stored and generated by the subclasses10 ptsThis criterion is linked to a Learning OutcomeSubclasses created from Major10 ptsFull MarksAt least three subclasses created based on Major, including one subclass extended from a subclass of Major5 ptsPartial CreditOne or two subclasses created based on Major, including one subclass extended from a subclass of Major0 ptsNo MarksOne or no subclass(es) created based on Major10 ptsThis criterion is linked to a Learning OutcomeSubclasses of Major have appropriate instance variables10 ptsFull MarksSubclasses of Major use the private instance variables in Major when possible, accessing them using getter/setter methods5 ptsPartial CreditSubclasses of Major use some the private instance variables in Major but also have local variables for some instance variables that should be in Major0 ptsNo MarksSubclasses of Major use all local variables rather than using the instance variables from Major10 ptsThis criterion is linked to a Learning OutcomeAll instance variable are private and initialized in the constructor in all object classes10 ptsFull MarksAll instance variable are private and initialized in the constructor in all object classes5 ptsPartial CreditSome instance variables are private or only some are initialized in the constructor0 ptsNo MarksNo instance variables are private or no instance variables are initialized in the constructor10 ptsThis criterion is linked to a Learning OutcomeAt least 2 of the 3 extended classes should have a minimum of two additional attributes and one additional method not found in Major and unique to the subclass10 ptsFull MarksTwo of the 3 extended classes have a minimum of two additional attributes and one additional method not found in Major and unique to the subclass5 ptsPartial CreditOne of the 3 extended classes has a minimum of one additional attribute and one additional method not found in Major and unique to the subclass0 ptsNo MarksNone of the extended classes have a minimum of one additional instance method not found in Major and unique to the subclass10 ptsThis criterion is linked to a Learning OutcomeThe toString() method overridden appropriately10 ptsFull MarksThe toString() method is overridden and implemented appropriately in the subclasses0 ptsNo MarksThe toString() method is not overridden and implemented appropriately in the subclasses10 ptsThis criterion is linked to a Learning OutcomeCode compiles and runs without errors5 ptsFull MarksCode compiles and runs without errors0 ptsNo MarksCode does not compile and is automatically assigned 0 points for the overall grade5 ptsThis criterion is linked to a Learning OutcomeCode is properly commented10 ptsFull MarksCode is properly commented including all required header information5 ptsPartial CreditCode has some of the required comments but is sparsely commented or missing the correct header information0 ptsNo MarksCode is sparsely commented or not commented at all and is missing all or most of the correct header information10 ptsThis criterion is linked to a Learning OutcomeProgram is properly designed and uses whitespace effectively15 ptsFull MarksProgram is properly designed and uses whitespace effectively8 ptsPartial CreditProgram is not properly designed or it does not use whitespace effectively0 ptsNo MarksProgram is not properly designed and it does not use whitespace effectively15 ptsTotal Points: 100

This assignment will allow you to demonstrate your understanding of the importance of training for security development lifecycle (SDL) processes. For this assignment, you are to discuss the importance of security training in preparation for software development. Using the online materials found in the second week content, as well as other materials gathered through UMGC library searches, provide five reasons for the importance of security training prior to executing the SDL process. Your discussion should provide details and specific examples for your selections. Your report should be well-organized, clearly defining and explaining each of the five reasons you selected.