Here, I have attached assignment details, notes, excel file and week learning materials that will help ut to do the assignment easily.
Why this assignment? • Opportunity to apply theory into practice • Exposure to real-life scenario • Develop meta-cognitive skills by reflecting on feedback What are the types of skills that I will acquire upon completion of this assignment? • Written communication skills • Think critically and reflectively. • Practical skills using Tableau and Excel • Practical skills using Knime Analytics Platform • Application knowledge in solving problems. • Self-management skills. Assessment Overview: This assessment is designed to develop students’ skills in the correct usage of analytical techniques and interpreting data for making managerial decisions. The main task is to analyse business data and to prepare a report for management based on an analysis of the data. The focus is on understanding the use of data analytical tools in a business context and develop written communication skills. 2 Assignment-3 tasks and description Tasks Steps/Description Which tools to use to complete? How to Submit your Work Construct classification tool Construct classification tool to effectively assist the buyer in identification of cars likely to be Kicks. • Experiment different configurations of the decision tree tool in Knime to find the best one you can. (NB. The error rate should be less than 15%). • It is expected that while exploring this tool, you may need to keep coming back to explore the dataset to find the best set of inputs for your classification problem#. Use Knime software Data file (Excel), Knime file (of your decision tree) on ePortfolio** Create Dashboard Create dashboard • When you are happy with your classification tool, create a dashboard in Tableau or Excel to present these inputs and how they affect IsBadBuy (Kicks). • Be mindful to choose appropriate visuals for your dashboard. Use Tableau or Excel Dashboard (Tableau or Excel) on ePortfolio** Write a 1000-word report What to include in report? Once successfully creating a classification tool, describe the tool’s functionality with respect to input contributions to the Kick classification. • Evaluate your classification tool and explain how it may assist the buyer to reduce the Kicks rate. • Using the data analytic methods you have learnt in the whole semester, explain your analysis, interpretation in the experiment to support decision makers. Use Word Combine item 3 and 4 and submit it on Turnitin Self-reflection on feedback from assignment-2 Reflection Proforma: Use the reflection proforma included in this document to complete your self-reflection and attach it at the end of your report (item-3 above) Use Word Note: # use Excel clean dataset and Data Dictionary from Assignment-2 **Refer to LEO on instructions on how to submit files on ePortfolio (Same process as assignment -2) Refer to case study (page-6) – the same case study as before. Refer to rubric for weight allocation and marking schema (Page-5 in this document) Case Study: Don’t Get Kicked (Same as assignment-2) One of the biggest challenges of an auto dealership purchasing a used car at an auto auction is the risk that the vehicle might have serious issues that prevent it from being sold to customers. The auto community calls these unfortunate purchases "kicks". Kicked cars often result when there are tampered odometers, mechanical issues the dealer is not able to address, issues with getting the vehicle title from the seller, or some other unforeseen problem. Kicked cars can be very costly to dealers after transportation cost, throw-away repair work, and market losses in reselling the vehicle. Data analysts who can figure out which cars have a higher risk of being kick can provide real value to dealerships trying to provide the best inventory selection possible to their customers. The challenge of this case study is to predict if a car purchased at an Auction is a Kick (bad buy). The data dictionary, Carvana_Data_Dictionary.txt, and the data files can be downloaded from LEO under Assessment tab. The data dictionary describes the 34 attributes: RefId, in the first column, contains the ID number for each record. IsBadBuy, in the second column, is the binary dependent variable, where a 1 (one) means “is Kick” and 0 (zero) “is not Kick”. The remaining columns (3 through 30) are independent variables. The dataset contains records for 72,561 vehicles, of which 12.3% are Kick. (Adapted from Kaggle competition) Important notes for assignment 3 · 1000 word report writing · Case scenario – same data set, data cleaning and data pre processing · Individualisation – dashboard (multiple charts, either Excel or Tableau) · Data classification tool – Knime software (understand from week 9 materials) free download · Study Weeks 7-9 materials · Reflection – last page of the assessment 3 specifications and must be included in the report · Analyse the case study once again and find out multiple problems and problem causes · How to submit – upload Excel or Tableau,Knime file, paste secret link into Word doc and upload into LEO. Same procedures from assignment 2 · Dashboard · Contain relevant charts from Week 7 · Spreadsheet – data cleaning / pre processing · Each image should have a caption · Report – appropriate format · Data analytical tools – Excel or Tableau · Reflect from the beginning before questions · Classification tool – experiment decision tree and error rate should be less than 15%. · To calculate the kick rate for each chosen input, only use and show the decision tree in the Knime software. · To calculate kick rate for auction field = Total no of kicks / Total no of cars (for each auction site) · In report writing, must explain interpretation of Tableau/Excel sheet dashboard, how they arrive at the input data file and interpretation of the outcome of the decision tree. · Dashboard – must provide reasonable and sensible charts (multiple charts), that chart should show Kicks rate · Knime decision tree – must have a working workflow with correct class column, class column of the tree learner should be is ‘bad buy’. Needs to be less than 15% (if 15-20% the marker will still accept it) · Report writing – always write in 3rd person, avoid ‘I’, ‘we’, ‘she’ words etc. Provide good reasons of your choices in relation to data analysis (Wk 7) and data mining (Wk 7) · Describe how data analysis you did and explain why? · Must conclusion the report as a data analyst · In Knime, you must check the confusion matrix in the scorer and try to improve the error rate · Charts should help the decision marker with solving the problem Report should contain: - in report, do introduction, theory component of the classification tool you've used, the analysis/interpretation of the classification tool and conclusion Important notes:- Here I have attached Week7 to Week 9 files that will support for this assignment. Also here I have attached excel file for the calculations. PPT_Template_16_9_2017 Office | Faculty | Department 1 | Week 7 Data visualisation and Reporting DATA201 Data Analytics and Decision Making Prepared by Dr. Thuy-Linh Nguyen based on Sharda et al (2018) Fast track your degree and study two units in Rome: 20 January – 2 February 2019* BIPX202 Community Engagement: Building Strengths and Capabilities • Undertake an international community engagement placement BUSN304 Working with Diversity and Conflict • Learn about communicating working internationally Places on this program are strictly limited and will be allocated on a first come basis. Total costs are anticipated to be approximately $2,600-$2,800 (tbc). Students may be eligible for travel assistance via the Vice Chancellor & President Travel Grant ($500) For more information on enrolment contact
[email protected] * Course offerings are subjects to student numbers mailto:
[email protected] Office | Faculty | Department 3 | Why are we doing this? By completing the activities in this week you should be able to: • Describe basic and specialised charts and graphs • Evaluate different chart options and pick a suitable one for your need • Explain what a dashboard is, its characteristics, and best dashboard practice • Design and create a basic dashboard • Explain the role of Business Reporting in managerial decision making • Identify major types of Business Reporting These weekly ILOs will help you achieve the unit ILO: • LO3 Assess and schematize the technical issues present in the stages of a data analysis task and the properties of different technologies and tools that can be used to deal with the issues (GA4, GA5, GA8, GA10) Office | Faculty | Department 4 | Essential Question What and how can I use data visualisation to effectively tell my story and support managerial decision making? 4 | Office | Faculty | Department 5 | Data visualisation Definition • “The use of visual representations to explore, make sense of, and communicate data.” Data visualization vs. Information visualization • Information = aggregation, summarization, and contextualization of data • Related to information graphics, scientific visualization, and statistical graphics • Often includes charts, graphs, illustrations, … Office | Faculty | Department 6 | A brief history • Data visualization can date back to the second century AD • Most developments have occurred in the last two and a half centuries • Until recently it was not recognized as a discipline • Today’s most popular visual forms date back a few centuries Office | Faculty | Department 7 | The first pie chart Created by William Playfair in 1801; who is widely credited as the inventor of the modern chart Office | Faculty | Department 8 | Decimation of Napoleon’s Army during the 1812 Russian Campaign Created by Charles Joseph Minard; arguably the most popular multi- dimensional chart Office | Faculty | Department 9 | Basic charts and graphs Office | Faculty | Department 10 | Specialised charts and graphs Office | Faculty | Department 11 | Which chart or graph should you use? Office | Faculty | Department 12 | An example Gapminder chart – Wealth and health of nations See Gapminder.org for interesting animated examples. https://www.gapminder.org/tools/#$chart-type=bubbles Office | Faculty | Department 13 | The emergence of Data Visualisation and Visual Analytics • Magic Quadrant for Business Intelligence and Analytics platforms (Source: Gartner.com) • Tableau, Microsoft, Qlik are in the Lead. Emerging companies in the Niche quadrant. • There is an increasing growth toward data visualisation