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Victorian Institute of Technology (VIT) – Melbourne offers student assessments to assess their knowledge of the MITS5509 Intelligent Systems for Analytics subject. In the intelligent Systems for Analytics Assignment, there is a task that every student need to clear in order to showcase their proficiency in the subject.

AssessmentsOverviewDue date
Assessment 1: Case Study and PresentationIn this assignment, you will be given a small case study and you will need to apply your knowledge to analyse the case and design the architecture of the Business intelligence system.Session 5
Assessment 2: Research ReportIn this assessment, you will write a critique or report on an academic paper(s) approved by your lecturer in the field of Business Intelligence, intelligent Analytics, analytic modelling or another relevant field.Session 8
Assessment 3: Major AssignmentIn this assessment, you will work in groups on a major practical based case to analyse the analytical requirements of the case study, design and architecture for an implementation of the business intelligence solution and develop elements of the solution.Session 12
Assessment 4: Final AssessmentDuring the end of the semester exam period

Assessment 1: Case Study and Presentation

Objective

This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment is designed to give students experience in analyzing a suitable dataset and creating different visualizations in the dashboard and to improve student presentation skills relevant to the Unit of Study subject matter.

Task

  1. Find a data set from an open data source website. Example:

https://data.gov.au/

https://www.springboard.com/blog/free-public-data-sets-data-science-project/ https://www.dataquest.io/blog/free-datasets-for-projects/ https://www.kaggle.com/datasets

  • Create DASHBOARD and present your insights including some basic analytics and four different visualisations. The student can use any software to create the dashboard such as Microsoft Excel, Power BI, Tableau, etc.
  • This is now an individual assignment, not a group assignment.
  • You need to record your presentation, for 3-6 minutes. You and your slides should be clear in the video file.
  • Submit the video file of your presentation in the provided link by the due date. The only original file will be accepted; a link to your video file will not be marked.

Presentation Requirements

The presentation must include,

  1. Brief description of the dataset and the reference link
  2. Method of data acquisition to the software
  3. Create four different visualizations for the dashboard
  4. Summary of the visualizations and how they could be used in predictive decision making

Submission Requirements

  1. Students need to record their presentation video and upload it on Moodle.
  2. The student must appear the entire time of the video.
  3. They may use any tool/software to record their presentations, the file size limit on Moodle is 200MB.
  4. Please make sure that your video is one of the popular video formats (i.e. .mp4).

Submission Instructions

All submissions are to be submitted through the assignment 1 Drop-boxes that will be set up in the Moodle account for this Unit of Study. Assignments not submitted through these drop boxes will not be considered. Submissions must be made by the due date and time (which will be in the session detailed above) and determined by your Unit coordinator. Submissions made after the due date and time will be penalized at the rate of 10% per day (including weekend days).

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Assessment 2: Research Report

Introduction

This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment is designed to give students experience in researching a topic and writing a report relevant to the Unit of Study subject matter.

Task

For this component, you will prepare a report or critique on an academic paper related to Intelligent Systems for Analytics or Intelligent Systems. Some possible topic areas include but are not limited to:

  • Intelligent Systems for Data Warehouse systems
  • Evolving Intelligent Systems: Methods, Learning, & Applications
  • Distance Metric Learning in Intelligent Systems
  • Intelligent Systems for Socially Aware Computing
  • Data Mining techniques with IS
  • frameworks for integrating AI and data mining
  • Expert System
  • Structure of knowledge Engineering
  • IS and Support Vector Machines
  • IS and Neural Network Architectures
  • Heuristic Search Methods
  • Genetic Algorithms and Developing GA Applications

The paper can be from any academic conference or other relevant Journal or online sources such as Google Scholar, Academic department repositories etc. All students must select a different paper. You can discuss with your lecturer before week 6 to decide on a topic. The topic needs to be chosen before the week

  • Students may discuss their chosen topics/papers in the discussion forum and may avoid similar papers selected by multiple students. The paper you chose should be published in the last 7 years (must be published in 2015 or after). Note: popular magazine articles, websites and blogs are not academic sources.

Your report should be limited to approx. 1500 words (not including references). Use 1.5 spacing with a 12-point Times New Roman font. Though your paper will largely be based on the chosen article, you can use other sources to support your discussion. Thus, citation of sources is mandatory and must be in the IEEE style.

Report Content

Title Page: The title of the assessment, the name of the paper you are reviewing and its authors, and your name and student ID.

Introduction: A statement of the purpose for your report and a brief outline of how you will discuss the selected article (one or two paragraphs). Make sure to identify the article being reviewed.

Body of Report: Describe the intention and content of the article. Document a critical analysis and clearly identify the workflow of the business process, the organizational units, actors, process relationships relevant to your chosen paper. Moreover, critically describe the adopted business process model, method and/or business process management tool which has been developed and applied in your chosen paper. In addition to that, report the approach to diagnose the root causes of poor process performance and recommend appropriate managerial levers for improving them. If such analysis and recommendation are not outlined in your chosen paper, discuss and justify your own view.

  • Conclusion: A summary of the points you have made in the body of the paper. The conclusion should not introduce any ‘new’ material that was not discussed in the body of the paper. (One or two paragraphs)
  • References: A list of sources used in your text. They should be listed alphabetically by (first) the author’s family name. Follow the IEEE style.
  • The footer must include your name, student ID, and the page number.

Resources

There are a number of resources you can utilize for finding academic articles and you should make the most of these resources.

  1. The VIT Library has access to an extensive array of online journals including IEEE and ACM journals. You can access these online Journals through the library’s subscription to the ProQuest platform. You can access this platform by finding the ‘ProQuest academic platform’ link on the StudyBoard@vit home page, towards the bottom of the page in the left column under the heading Resources & Services.
    • Please also refer to the link in the same section on ‘Scholarly Article Searching’. This will show you how to search for articles of the type required for

this assignment.

  • In the same section, additionally, refer to the link ‘VIT Library Referencing’. This link discusses a number of referencing styles. Different types of Journals will require different styles of referencing and citation, but this assignment requires the IEEE style. Commonly associated with Engineering and Science disciplines. This style is discussed in the document and provides examples and links to further resources.
    • Google Scholar ‘https://scholar.google.com.au/‘ is a separate google search engine that will help you search for scholarly articles (Journal articles and conference papers etc..). It will generally not have access to the resources that Proquest above does as these are usually paid sites. However, Google Scholar does index an extremely large amount of scholarly articles located in open access conferences, Journals and academic websites. Please go through the above notes ‘Scholarly Article Searching’ to find out how to use Google Scholar.

Submission Instructions

All submissions are to be submitted through turn-it-in. Drop-boxes linked to turn-it-in will be set up in the Unit of Study Moodle account. Assignments not submitted through these drop-boxes will not be considered.

Submissions must be made by the due date and time (which will be in the session detailed above) and determined by your Unit coordinator. Submissions made after the due date and time will be penalized at the rate of 10% per day (including weekend days).

The turn-it-in similarity score will be used in determining the level of plagiarism. Turn-it-in will check conference websites, Journal articles, the Web and your own class member submissions for plagiarism. You can see your turn-it-in similarity score when you submit your assignment to the appropriate drop-box. If this is a concern you will have a chance to change your assignment and re-submit. However, re-submission is only allowed prior to the submission due date and time. After the due date and time have elapsed you cannot make re-submissions and you will have to live with the similarity score as there will be no chance for changing. Thus, plan early and submit early to take advantage of this feature. You can make multiple submissions, but please remember we only see the last submission, and the date and time you submitted will be taken from that submission.

Your document should be a single word or pdf document containing your report

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Assessment 3: Major Assignment

Introduction

This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment is designed to improve student collaborative skills in a team environment and to give students experience in constructing a range of documents as deliverables from different stages of the Intelligent Systems for Analytics.

Task

This assignment is to be completed in teams of 3 or 4 students. You should begin by submitting (at the end of week 7) the signed group participation form provided in Moodle. This form needs to be completed and signed by all group members. Once submitted, the teams will remain unchanged and no member additions or deletions will be allowed unless by approval of your subject coordinator. Any person not part of a group by the end of week 7 will be assigned randomly to a group by your lecturer. There will be no changing this.

Carefully read the following two questions and provide the appropriate answer.

Question 1:

The age and stage problem can be viewed as a problem of classification. The data set you will be using for this problem includes the age of a person and the stage of that person: Teenager and Adult (See the attachment): In total, you have 1000 datasets containing two columns: Age and Stage. Your goal is to use different classifiers to build a training model, by randomly selecting the 80% data points, and then test its performance on the testing model by randomly selecting 20% data points from the testing set.

Students must use the following classifiers. The selection of the classifiers depends upon the members of the group, e.g. if the group has four members then they will use the four classifiers from the following five classifiers.

  1. Neural network
  • Support vector machine
  • Nearest Neighbour algorithm
  • Decision tree
  • Naive Bayes

The group has to prepare a report which includes the followings:

  1. Explain the process of building each classifier using the training dataset (add the screenshots).
  2. Create the confusion matrix based on 80% (training) / 20% (testing).
  3. Explain how you evaluated the classifier.
  4. Predict the category of the values (any random 200 values) in the table used for the Testing set.
  5. Compare the results between the different classifiers and discuss which one is the best and why.

Note: Students can use any open source free data mining software such as Python, Statistica Data Miner, Weka, RapidMiner, KNIME and MATLAB etc.

Question 2:

Create a DASHBOARD. For creating a dashboard, the group can use any dataset. The group has to prepare a report which includes the followings:

  1. Write an introduction about the dataset used and add the reference (link).
  2. Create at least four figures (different graphs) and add them to the dashboard.
  3. Add a Screenshot of each of the steps.
  4. Describe the figures in the dashboard.

Note: Students can use any software to create the dashboard such as Microsoft Excel, Power BI, Tableau, etc.

Submission Requirements

Your report must include a Title Page with the title of the Assignment and the name and ID numbers of all group members. A contents page showing page numbers and titles of all major sections of the report. All Figures included must have captions and Figure numbers and be referenced within the document. Captions for figures placed below the figure, captions for tables placed above the table. Include a footer with the page number. Your report should use 1.5 spacing with a 12 point Times New Roman font. Include references where appropriate. Citation of sources (if using any) is mandatory and must be in the IEEE style.

Only one submission is to be made per group. The group must select a member to submit the assignment by the due date and time. All members of the group will receive the same grade unless a special arrangement is made due to group conflicts. Any conflict should be resolved by the group, but failing that, please contact your lecturer who will then resolve any issues which may involve the specific assignment of work tasks or the removal of group members.

Submission Instructions

All submissions are to be submitted through turn-it-in. Drop-boxes linked to turn-it-in will be set up in the Unit of Study Moodle account. Assignments not submitted through these drop boxes will not be considered.

Submissions must be made by the due date and time (which will be in the session detailed above) and determined by your Unit coordinator. Submissions made after the due date and time will be penalized at the rate of 10% per day (including weekend days).

The turn-it-in similarity score will be used in determining the level of plagiarism. Turn-it-in will check conference websites, Journal articles, the Web and your own class member submissions for plagiarism. You can see your turn-it-in similarity score when you submit your assignment to the appropriate drop-box. If this is a concern you will have a chance to change your assignment and re-submit. However, re-submission is only allowed prior to the submission due date and time. After the due date and time have elapsed you cannot make re-submissions and you will have to live with the similarity score as there will be no chance for changing. Thus, plan early and submit early to take advantage of this feature. You can make multiple submissions, but please remember we only see the last submission, and the date and time you submitted will be taken from that submission.

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