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8. Software Updates and Versions:

Tableau regularly updates its software, which can lead to compatibility issues with older versions. Students might face difficulties when their assignments require specific software versions or features that are no longer supported.

Tableau Topics & Concepts Covered

TOPICS CONCEPTS
Data Connection Data Sources, Preparation, Blending
Data Visualization Charts, Graphs, Maps
Data Exploration Sorting & Filtering
Dashboard Design Interactivity and User Experience
Data Preparation Data Transformations & ETL
Calculations Level of Detail (LOD) Expressions
Server & Sharing Publishing & Sharing Workbooks
Advanced Analytics Clustering & Segmentation

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Recently Asked Tableau Questions

Expert help when you need it
  • Q1:Analyze our team: How many salespeople are meeting their goal? Who are the top 4 performers? After we identify them, we can assign them as mentors to the rest of our salesforce. We have 28 total salespeople, so each mentor will get 7 people. List our top 10 most profitable stores? What are they selling? What is the income level in that area? That can help us predict what will sell in other areas. Where do most of our orders come from? (Look at our sales channels) Analyze our products: What are our top 5 bestsellers, in terms of profit? What are our top 5 bestsellers in terms of popularity? Analyze our market: Who are our top 5 customers, in terms of profit? What are these customers buying? Rank our regions in terms of profitability. What items are most popular in each region. Rank our states in terms of profitability. What items are most popular in each state? For each region: What products are bringing in the most money? What items are most popular? We want to keep these regions stocked with those products. For each state: What products are bringing in the most money? What items are most popular? We want to keep these regions stocked with those products. What about seasonal trends? Are some products more popular at certain times of the year than others?See Answer
  • Q2:Deliverables: 1) Go through the dataset (excel file). 2) Go through the "question instruction file" 3) Create visualisation in Tableau. 4) Write a story identifying injury pattern of two age groups (young and old) and suggest how to avoid such injuries for these groups. 5) Write 1 page for each group. EACH STORY MUST CONTAIN THE USE OF MULTIPLE PRODUCTS AND BODY PARTS.See Answer
  • Q3:Case Questions Here is a list of questions that you will be answering for this case. Complete instructions for how to construct the interactive Tableau dashboard are in the tutorial video that accompanies this project. Please read this student case handout in its entirety before starting the case. Use the data dictionary provided on page 1 of these instructions and the interactive Tableau dashboard file to answer the following questions: 1. What were total sales revenues for the 1st quarter of 2020? 2. What were the total sales revenues for the Medical category in the 3rd quarter of 2021? 3. Which product line had the highest sales in 2020 under the materials processing category? 4. What is the top product line for the Germany division in 2019? 5. In 2021, which quarter had the highest sales in the Telecom category, under the UK Division? What was the dollar amount? 6. Who is KAT Manufacturing's top customer in the 4th quarter of 2019? 7. Who is the top customer for the Germany division in 2021? 8. If you were on the top management team of KAT Manufacturing, what overall strategy questions could this dashboard help you address? Answer this in your video assignment!See Answer
  • Q4:Question 6 (This relies on the file iris.csv that can be found in LumiNUS. Use that file to avoid problems associated with different versions.) p-norms are used to measure the distance between multi-dimensional data points and the origin. For a n-dimensional data point x = (X₁, X2, ..... Xn), the p-norm is given by: 11/p n ΣX₂² k=1 See: https://en.wikipedia.org/wiki/Lp_space#The_p-norm_in_finite_dimensions Here we will, for each type of flower (setosa, versicolor, and virginica), measure the distance between each data point in the 1-norm, 2-norm, and 3-norm from the mean of each of the factors: Sepal Length, Sepal Width, Petal Length, and Petal Width. So each data point is in 4-dimensions, and the distance from each data point from the mean from is in 4-dimensions The number of data points where the (component-wise) difference of the data point from the mean for its flower type has a p-norm less than or equal to 1.5 is: Type of Flower \p setosa FLAG QUESTION versicolor virginica 2 FYI: Depending on which items were selected for this assignment in this semester, there may or may not be another question that tells you do do the exact same thing for with a different threshold. So create your visual accordingly. Note: The 1-norm is the Manhattan distance, which is quite relevant in transportation operations in cities. The 2-norm is the usual straight line distance. In analytics work, it is common to generalise well known metrics.See Answer
  • Q5:(This relies on the file Sample - Superstore.xlsx and postal-code-delivery-data.xlsx that can be found in LumiNUS. Use those files to avoid problems associated with different versions.) (This question might be viewed as the first of two independent parts of the Delivery from the Customer Perspective question. As the ordering of questions may not be sequential, you may want to check if the other independent part was included in the assessment this semester.) You are managing fulfilment at a nationwide retailer in the USA. You are interested in the customer experience but have limited data on fulfilment due to late adoption of supply chain management methodologies and technology. If an order ships the same day, we take the gap between ordering and shipping to be 0.25; if an order ships the day after it was ordered, we take the gap between ordering and shipping to be 1.25; if an order ships two days after it was ordered, we take the gap between ordering and shipping to be 2.25; and so on. There is data on average timings between orders being shipped and arrival at the customer's address in postal-code-delivery-data.xlsx. If there is missing data on the Average Delivery Lead Time (Days) (time from shipment to arrival at a customer's address) for a given Postal Code, you will fill in the missing value with 3.5, a conservative value for the entire country. Calculate the average End to End lead time between orders being placed and deliveries arriving at the customer address (in days) for the following states: Alabama: (1) • Arkansas: (2) • California: • Colorado: • Mississippi: Give your answers to 2 decimal places. Note: In this question, Ship Mode does not matter. SX int: Postal Codes can be tricky to work with. There is at least some postal code data for New Jersey and New Hampshire. Hint: Be careful to avoid double counting since there might be multiple rows from the same order.See Answer
  • Q6:SX Question 9 FLAG QUESTION (This relies on the file Sample - Superstore.xlsx and postal-code-delivery-data.xlsx that can be found in LumiNUS. Use those files to avoid problems associated with different versions.) (This question might be viewed as the second of two independent parts of the Delivery from the Customer Perspective question. As the ordering of questions may not be sequential, you may want to check if the other independent part was included in the assessment this semester.) You are managing fulfilment at a nationwide retailer in the USA. You are interested in the customer experience but have limited data on fulfilment due to late adoption of supply chain management methodologies and technology. If an order ships the same day, we take the gap between ordering and shipping to be 0.25; if an order ships the day after it was ordered, we take the gap between ordering and shipping to be 1.25; if an order ships two days after it was ordered, we take the gap between ordering and shipping to be 2.25; and so on. There is data on average timings between orders being shipped and arrival at the customer's address in postal-code-delivery-data.xlsx. If there is missing data on the Average Delivery Lead Time (Days) (time from shipment to arrival at a customer's address) for a given Postal Code, you will fill in the missing value with the current average across all orders in the state for which there is data (this has to be weighted against the number of orders). If there is no data for the entire state, then use 3.5, a conservative value for the entire country. Calculate the average End to End lead time between orders being placed and deliveries arriving at the customer address (in days) for the following states: • Alabama: • Arkansas: (2) • California: • Colorado: • Mississippi: 5 Give your answers to 2 decimal places. Note: In this question, Ship Mode does not matter. Hint: Postal Codes can be tricky to work with. There is at least some postal code data for New Jersey and New Hampshire. Hint: Be careful to avoid double counting since there might be multiple rows from the same order.See Answer
  • Q7:Task: Create a bar chart of net energy producing regions in the first visualization (Net Production). a. Create a calculated field called NetProduction. It should perform the following operation to compute the surplus or deficit in energy production: SUM([Net Generation]) - SUM([Demand]) b. Create a sorted bar chart, in descending order, of net production by region. c. Identify which regions are net producers in the Write Answers Here sheet.See Answer
  • Q8:Tableau Storytelling Assignment: prevent customers from leaving Marks: 15% Learning Objectives evaluated: 1. Learn the fundamental concepts of storytelling 2. Learn the principles of visualizations 3. Apply visualization principles to create effective visualizations 4. Learn how to organize and prepare data prior to analysis 5. Apply storytelling concepts to write effective stories with data AirTel Corp. is a telecommunication organization providing internet and phone services. The telecommunication market is highly competitive, and customers often switch from one organization to another. Your task is to come up with specific strategies to prevent a group of customers from leaving the organization. Tasks You will write a one-page story that will answer the following question. 1. Based on a specific profile of customer, what specific strategies will you suggest stopping them from leaving the organization? Please see the following details for this assignment. 1. This is an assignment to be done in pairs. Make sure that both partners contribute to the assignment. Mention in a separate paragraph, the responsibilities of each member of the team 2. You must use Tableau to do this analysis also Excel can be used to organize/format the data 3. The assignment MUST include two and a maximum of four visualizations based in Tableau 4. The page limit of this story is one page including visualizations. You can consider this assignment as a poster with visuals and text. 5. Submit only the assignment document on canvas and not the dataset. Do not upload the visualizations separately. 6. Follow the principles/concepts of storytelling and visualizations covered in modules 1 and 2.See Answer
  • Q9:DELIVERABLES: 1) There are 4 dashboards attached (ch2, ch3, ch13, ch14), we need to write 1 page summary for each dashboard. Choose any 2 for part1 and other 2 for part2. 2) For the Second Section, read the instructions, go through the data sets attached and provide with what is asked, following all the instructions.See Answer
  • Q10:Planning - Part 1 Planning is the first step towards creating a dashboard. For this step you will need to: 1. Explore the various repositories and check multiple datasets. For each dataset, o Read its description. o Find the number of rows and columns. o Check the column data types. 0 If available, check what other data scientists used this dataset for. o Think about what can be visualized about this dataset. https://www.kaggle.com/datasets https://www.kdd.org/kdd-cup https://population.un.org/wpp/Download/Standard/Population/ https://grouplens.org/datasets/ 2. Select a dataset of your choice & download it. Explain what this dataset is about. 3. For each column, specify its data type: Categorical, Ordinal, Interval or Ratio. 4. For each column, specify its domain; that is the list or range of values that it can take. 5. Think about who would like to use a dashboard to create visualizations about this dataset. o For example, a card fraud dashboard can be used by bank cybersecurity teams. o A Parkinson's dashboard can be used by physicians and health care providers. 6. List the prospective users that you will develop the dashboard for, and what do you think they can use this dashboard for. 7. List a comprehensive set of questions that the users might ask about this dataset. o This is not the final set of questions that the dashboard will address. o It is just a starting point and may contain much more questions than what the dashboard will finally address. So list as many questions as you may think of./nProject - Part 1 Submission Submit a report in which you answer the questions listed above. The report should have three main sections: Section 1: Dataset Description (10 points) Section 2: Prospective Dashboard Users (10 points) Section 3: List of User Requirements & Potential Questions. (20 points) Decision Making - Part 2 Now that you understand your data, the users that are interested in it and what sorts of questions they might have about the data, it is time to make decisions related to how the actual dashboard will look like. The decisions that you will make are for two main subjects:/nChoosing Visualization Tools List the visualization tools that you will use to create the dashboard. Explain why you chose these tools. This can be due to data-related issues or personal preference of certain development tools. Explain why you prefer some tools over others. Data Preparation & Preprocessing In case your data requires any kind of pre-processing such as computing certain attributes or removing missing values, explain how the data will be processed and prepared for visualization. Final Set of Questions List the final set of questions that your dashboard will be designed address. The dashboard users should be able to find answers for these questions by using your dashboard. List at least five questions./nChoosing Visualization Tools List the visualization tools that you will use to create the dashboard. Explain why you chose these tools. This can be due to data-related issues or personal preference of certain development tools. Explain why you prefer some tools over others. Data Preparation & Preprocessing In case your data requires any kind of pre-processing such as computing certain attributes or removing missing values, explain how the data will be processed and prepared for visualization. Final Set of Questions List the final set of questions that your dashboard will be designed address. The dashboard users should be able to find answers for these questions by using your dashboard. List at least five questions./nList of Plots For each of the questions listed above, think about the best plots that can be used to address it. Keep in mind that one question might require multiple plots to address. Alternatively, one plot can address multiple questions. The dashboard should contain at least five plots. For each plot, • • Explain what it shows and how that relates to the set of questions. List the set of used pre-attentive attributes and colors. Include a rough, hand-drawn or computer-drawn, figure of the plot. List of Interactive Controls The dashboard user can change the visualizations via interactive controls. If your dashboard contains any controls. • List what they will be used for Which plots are connected to each one • The value range for each control and whether or not it is loaded from a certain attribute in the data. Project - Part 2 Submission Submit a report in which you answer the questions listed above. The first page of the report should contain the student name & the project title. The report should have five sections: Section 1: Used Visualization Tools • Section 2- Explanation of Required Data Pre-processing if any/nPART 3 General Project Information • The final deliverables are: o The dashboard o A report that includes all answers to questions listed in the three project phases. . A power point presentation. Details for each deliverable will be provided in the relevant project phase. Implementation - Part 3 Now that have laid down the design of your dashboard, it is time to implement it! Step I: Create the Single Visualizations Create each visualization to match the pre-specified design. 1. Use consistent color palettes and the minimum number of pre-attentive attributes possible to covey your information. Step II: Add Interactivity Add controls, if any, to the visualizations. Choose the most user-friendly controls. 1. For example, if you have controls that allow the user to change a numeric value, and this value has a very wide range, e.g., all numbers from 1 to 200, it is better to use a sliding bar control and not a list or a drop down menu.See Answer
  • Q11:INSTRUCTIONS: The PDF with instructions has almost all the pictures demonstrating how to do it. Follow the instructions and provide with what is asked. Do not need any explanation report for this. As mentioned, I only need Screenshots.See Answer
  • Q12: Assignment #1 This assignment requires you analyze data about electricity demand and production in 28 European countries from 2000 to 2020. The data for this assignment is contained in the Excel file "Europe_Power_Gen". There are four variables: ● ● ● Year: The year the electricity was generated or used Country: Country name (28 values) Category: The demand for electricity, or the source/method used to generate electricity: O Demand -Total electricity demand for a given country and year o Bioenergy - Derived from recently living organic materials (renewable energy source) Coal - Carbon-based sedimentary rock burned to produce power (fossil fuel source) Gas Natural gas burned to produce power (fossil fuel) Hydro - Fast moving water spins turbine blades to generate power (renewable) Nuclear - Use of nuclear reactions to produce electricity (some people consider this "green") Other fossil - Fossil fuels other than coal or gas Other renewables - Renewable power sources other than bioenergy, hydro, solar, or wind Solar - Manufactured cells transform sunlight into electricity (renewable) Wind - Wind spins turbine blades to generate power (renewable) Generation (TWh) - amount of electricity generated or used (in terawatt hours) Demand for a given country and year is typically met by the sources/methods listed for that country in that year. That is, the value for Demand (listed in the Generation column) equals the sum of the values for the nine listed sources/methods (Demand = Bioenergy + Coal + + Wind). If electricity demand for a given country and year was not met by the sources listed, the untry imported the remaining power. If a country produced more than it needed, it exported the rest. A negative number for generation means that the country exported that amount of electricity from that source/method. Do not make any modifications to the Excel file. Perform any calculations, filtering, etc. in Tableau. Assignment Instructions 1. Use Tableau Desktop to create a visualization that shows the viewer how European electricity demand and/or production has changed over the years 2000 to 2020. Determine an interesting message or "story" contained in the data. That is, what observations and/or information are most important to show the reader? Determining what is most important is ultimately up to you, but it should be something that you consider significant, surprising, and/or unexpected. Perform your own analysis and create a visualization that conveys your findings effectively. Create the visualization for a general audience, but one that understands the variables and their values. 2. Create a static visualization – either a choropleth map or line chart. You do not have to use all of the countries, but you must use at least five of them. You may use a calculated field if desired. Do not add any additional data (anything that is automatically generated by Tableau can be used). Do not use customized tooltips or animations since this will be a static visualization. You may group related data values together (any groups that you create should be clearly noted on your visualization). You can also filter your data as needed or desired (the filter will not be displayed on the visualization graphic). 3. Follow the guidelines for good visualization design discussed in class and summarized on the slides. Implement your chart carefully. For example, use appropriate colors, titles, axis labels, legends, data labels, and notes to the reader (if needed). Ensure that the reader can quickly recognize and understand what you are trying to show them. Format any legends appropriately. Don't feel that you need to create an overly complex chart – sometimes simpler charts are most effective. Do provide a good title to help guide the reader in what you are showing with your visualization. 4. Generate an image file (.jpg) of your visualization (use Dashboard/Export Image). Be sure to leave the boxes for caption and legend checked. Also create a Tableau .TWBX file that contains your worksheet and the data. Upload these two files to Canvas by the assignment deadline. Double-check your submission in Canvas to ensure that you have uploaded the required files in the proper formats, and that the files were submitted successfully. 5. This assignment will be graded on a scale of A, B, C (potentially with a plus or minus). Items that will factor into your grade include 1) the type of chart used, 2) how well it effectively conveys the data, 3) how well the visualization is implemented, 4) well-written titles, legends, axis labels, data labels, and notes (as applicable), 5) good and proper use of color, 6) following the instructions above, and 7) attention to detail./nDescription Download the attached Excel data set along with the PDF file, which contains assignment instructions. Perform your data analysis using Tableau Desktop. When you are finished, upload the completed files specified in the instructions. CommentSee Answer
  • Q13: Olympics Data Analysis and Dashboard: Power BI Data Preparation and Dashboard Development Summary With Power BI "Transform Data," clean and prepare the Olympics data set. Then, with Power BI Desktop, plan and develop at least 4 professional quality dashboards plus a sheet that allows the user to drill down to lowest-level details contained in the data, to help someone generally knowledgeable about the Olympics, explore and learn more about: a) the Olympics as a whole (dashboard); b) the Olympics in a specific year and season that the user specifies (winter or summer) (dashboard); c) country-level information (user specifies the country) (dashboard); d) sport/event-level information (specified by the user), including information about individual athletes (dashboard); and e) a page in the PBI file that allows the user to interactively drill down to the lowest level details in the data. Prepare a brief writeup as part of the PBI file. Guidance Read the entire document before starting. Plan to go through two or three iterations of your dashboards. Start early enough so you can be thinking about how to improve the dashboards and make the document unified (i.e., common styles, look/feel, user interactions). Data Files and Making Connections to Data The zip file olympics.zip contains three CSV files. Two contain information about Olympic Games performances over many years (olympic_summer.csv, over 220,000 rows; and olympic_winter.csv, over 48,000 rows). These two files have the same schema (format/structure of data columns). The third CSV file (olympic_countries.csv; about 230 rows) contains country abbreviations and associated country names (see the Data Dictionary section below). Do not make any changes to the CSV files. Connect to these files from Power BI. You can certainly look at and explore them in a text editor and/or Excel, but when you load them to Power BI, connect to them as CSV files. It is best to keep the CSV files and your PBIX file in the same directory. It is important to realize that the PBIX file contains connections to data files. The PBIX file format is designed to both keep this connection information in addition to a copy of the data. However, the “true” source of the data is the CSV files, and whenever PBI refreshes the data (for example, in the Transform Data steps), it looks to the connection information. You will likely be working on this from several different computers. It is important, after copying the PBIX file to a new computer, to also update the "Data Source" settings in PBI. From the Home menu, pull the arrow down next to Transform Data, and choose "Data source settings." From there, select each CSV file and update the file location to where the file resides on your computer. This is the most important during the Transform Data phase (data preparation). You will not be able to do any Transform Data operations unless PBI can "find" the true source files. After your data is completely prepared, PBI can actually work with the copy of the data in the current PBIX file, but if the source data ever changes or you need to go into Transform Data again, you will need to update the Data Source settings so PBI can find the files. More information, not required if you follow the steps in the previous paragraph. It is possible to store the CSV files on a web server or shared resource, e.g., Google Drive, Sharepoint, etc. In my testing with these files, connections to Google Drive became quite slow due to the file sizes, and Sharepoint adds some Olympics Data Preparation and Dashboard Page 1 of 6 complexity that is beyond the current scope of this case. If you do choose to try this approach, make sure that the source files are readable by anyone...me...without needing a username/password. Tasks (see also the “Requirements for the Dashboards” section, below) Note for dashboards: After your clean/prepare the data, you will have 4 tables: summer, winter, combined, and countries. For the dashboards you will only use fields from the combined table and the countries table. The purpose of the combined table is so that we can have combined summer & winter results when desired, and just as easily filter out one season or the other when we don't. Do not create visuals with the winter and summer tables. 1. Prepare/clean the data (see Data Preparation Guidance, below). 2. Explore/Experiment. Spend time exploring the data by creating a number of visuals, tables, etc. Create one or more "Experimental” pages for this. Do enough of this to learn major aspects/insights you want to explore further, as well as nuances of the data that may not be obvious at first. 3. Overall Dashboad. Create an overall (i.e., top-level) dashboard (name the page "Overall"). The visuals must show at least the following: medals awarded over time, athletes competing over time, medal count by country, and athlete count by country. Include card(s) to summarize key values. You may want to present some of this on the same visual(s). Use slicers and/or filters to allow user to drill into the data as they choose to. Note that “athletes” competing over time can be interpreted several ways, e.g., unique people who have competed ever, unique people for each Olympic games, and total number of entrants into all events (this last one multi-counts athletes as people). Be clear in your visuals (on this page and all) whether you are talking about unique people, or event entrants. 4. Year/Season Level Dashboard ("Year and Season Persona"). For this dashboard, have the user select the year(s) and season(s) (use slicers or filters), and your dashboard will then present visuals that inform the user about that specific year(s)/season(s). 5. a. Note: For the dashboards in steps 4, 5, and 6, the idea is that the user will choose a specific year(s)/season(s) (or country(s), or sport(s)/event(s)). The visuals then should show details about that specific year/season (or country, or sport/event). Therefore, you would not want a visual, for example, that shows medals over time for the year/season dashboard, as most of the time the user will just be selecting a single year/season...that would make for a chart with one data point, i.e., without meaning. Think about what is meaningful, test it out with various settings as the user would do, and iterate; do not just click the buttons and accept defaults. Country Level Dashboard ("Country Persona"). Provide a way for the user to select a country (or countries), and then present information about that country and its participation in the Olympics. 6. Sport and Event Level Dashboard ("Sport and Event Persona"). Provide a way for the user to select a sport(s) and event(s), and then present information about that sport/event. Within sports, there are events, so this dashboard is more of a sport- and event-level dashboard. Ideally, the user should be able to drill down to specific events and see specific information on individual athletes. 7. Drill-down sheet. On this sheet provide a way for the user to intelligently and seamlessly drill down into the data to any level they wish, even to the individual record level in the data. You will probably need to experiment with a mix of graphs, decomposition tree, and table/matrix (a combination of a decomposition tree and a table or matrix is often a good way to provide drill-down capability). 8. Writeup. Create a "Writeup" page. Insert a text box. List/discuss three key insights you gained specific to the problem context (that is, what did you learn about the Olympics). Then list/discuss three key things you learned about visualization and/or Power BI in completing the assignment. Target length for this is the equivalent of a one-page writeup (more than just bullet points...give the main point and then explain, with examples to illustrate). 9. PBIX and PDF. Save your Power BI file as a PBIX file. Also generate a PDF of your PBI file by using the File...Export option. You will be submitting both. You do not need to submit the CSV files. Olympics Data Preparation and Dashboard Page 2 of 6 Guidelines and suggestions for the dashboards Spend time on the general layout and formatting of your first (overall) dashboard. That way you can duplicate the page and make changes for the other dashboards. While obviously you will make changes for each dashboard, there should be a reasonably common look/feel to your dashboards. Try to make visuals professional quality, titled/labeled appropriately, with appropriate color selections, and able to be understood by user without additional explanation. With visualization, getting something 70% done can be pretty quick, but the remaining 30% of tweaking settings, titles, colors, alignment, etc. is what often separates a professional-level job from a novice job. Slicers and filters should add meaningfully to the dashboard's value. Each dashboard should contain at least 4 visuals (for this count, slicers don't count as a visual, but you should have one or more slicers also; table/matrix does count as a visual). Each dashboard must have a brief text header for the title of the dashboard (put this in a text box). Be precise about language. For example, "athletes" (unique competitors) is different than "event entries" (all athletic entries in a competition), is different than "medals awarded," and is different than “medalists” (unique athletes who won at least one medal). For many/most charts, you will need to change the default titles to ensure the user knows exactly what the chart displays. You can distinguish between these (depending on the field) by summarizing as a Count versus the Count Distinct option. Create at least three measures and use them in cards or similar visuals (not required for every dashboard, but in total). ● Use at least one of each of the following visuals, across all your pages. This is not for every page, but taking all your pages together, utilize at least one of each type of the following visuals: timeline (line or area), ribbon, bar, column, scatter (xy), filled map, treemap, histogram (use data grouping and a column chart, not a custom visual), matrix, table, card (or multi-value card). Of course, you will use some visual types more often than others. The above is a requirement to have at least one of those in the list so you can experiment with the best chart(s) to show particular aspects. ● On every dashboard, use slicers (you will need several slicers for some pages) and/or page-level filters. Visual-level filters get added automatically for each visual for you to be able to tweak an individual visual and you may need to customize settings on some individual visuals. For this assignment, do not use the "Filters on all pages" capability (this affects all pages in the document, sort of like filtering out rows in Transform Data would do). Use the country code and country name intelligently. Do not assume the user will know the country codes. Submit ● All pages must be interactive, that is, clicking on one visual automatically cross-filters/highlights the others, and similarly for slicers and filters. PBIX file PDF file obtained by exporting the PBI file to a PDF file (File...Export). Data Preparation Guidance Change the names of the queries to Summer, Winter, and Countries. The default names are probably inherited from the rather long file identifier in the links provided earlier in this document. First row as headers. When importing text (e.g., CSV) files, the first row may not be automatically used for column headers. In Home tab of Query Editor, Use First Row As Headers option is used to promote the first row as headers. Winter and Summer Files (do these steps on each file) O Delete the "Changed Type" step that PBI likely adds automatically. With this data, PBI's default choices, result in errors for some of the columns (PBI looks at the first 200 rows to decide on data types, and this causes issues for at least one file here). Deleting this step and reassigning the appropriate data type (later) works better. Olympics Data Preparation and Dashboard Page 3 of 6 ● ● Create New column named Season. Set to "Summer" for summer table; "Winter" for winter table. The formula for this column is just = "Summer" or = "Winter", respectively when you are in the Add Column dialog box. Make sure you do this before appending the queries! Append the queries. O Appending queries combines two queries into one by combining the rows; this is why we needed a Season field in the previous step. In this stage you will combine winter and summer results. It is critical that you successfully complete the previous steps before doing this one. While in Query Editor ("Transform Data" in PBI), select the summer query. Go to Home Tab → Append Queries → Append Queries as New In the dialog box, the summer query should already be filled in one field. In the other field, select the winter query, and hit OK. Rename the resulting query to be "olympic_combined" and verify that you have all rows from both summer and winter in the combined query. O O O O Clean the data in the olympic_combined query O Age, Height, and Weight columns. Replace NA with blank (i.e., nothing; you can usually also type null without quotation marks). Convert to decimal number. You can do this one column at a time or select all three columns and do it at once. Query Editor will probably insert the null value for the empty values which is OK (it uses null to tell you that there is truly nothing entered, not even a space character). If you try to convert a column containing some text values to numeric, you will get errors. Year. Convert to whole number (don't try to convert to date; we're just interested in year and season so whole number is sufficient). Season. Convert to text. O Medal column. This column contains Gold, Silver, Bronze, or NA. In contrast to some other columns, NA is not missing data here. It means the athlete did not earn a medal. For clearer communication, replace this with something like "No Medal." Add a Conditional Column called Medal_Index. The Medal column is text, but it is really ordinal categorical data (i.e, categorical data with a natural order from best to worst). We need to be able to display results in Gold, Silver, Bronze, and "No Medal." To do this, create an index column based on the Medal column. Specifically, add a conditional column. Assign the value 1 to Gold, 2 to Silver, 3 to Bronze, and 4 to "No Medal" (see screen shot). After closing Query Editor, we instruct Power BI to sort the Medal column based on the value of the Medal_Index column (keep reading for instructions). Close and Apply to return to Power BI Model View. Create the connection between the "Olympic Code" column in the olympic_countries table and the "Country" column in the olympic_combined table. Go to the Model Tab and create a 1-to- many relationship between Olympic_Code in the olympic_countries table and Country in the olympic_overall table. Data View. In the Data View for the olympic_combined table, select the Medal column. Choose the Column Tools tab, and "Sort by Column" dropdown. Sort the Medal column by the value of the Medal_Index column. This does not immediately sort the Medal column. Rather, it tells Power BI that whenever Medal is included in a visual, that the medal names will be listed in order according to the Medal_Index column (you can choose ascending or descending). Check this when you create your first visual or table listing the medal types and counts. O O Olympics Data Preparation and Dashboard Page 4 of 6 Conditional Column Creation for Model_Index ● ● ● ● ● Add Conditional Column ● Add a conditional column that is computed from the other columns or values. New column name Medal_Index If Else If Else If Else If Else (0) 123 Column Name Medal Medal Medal Add Clause Medal null ● Operator equals equals equals equals Sport Event Year (of Olympics) City (of Olympics) Value > 123 123 Gold Silver 123 Bronze 123 No Medal Then Data Dictionary olympic_summer and olympic_winter files. Each row represents one entry into an event. Athlete Gender PBIX/td-p/800887 Output 12-1 ABC Then 123 Then 123 Then 2 3 4 ОК Age (some data is missing; see Data Preparation section) Height (cm) (some data is missing; see Data Preparation section) Weight (kg) (some data is missing; see Data Preparation section) Country abbreviation of athlete Cancel X Medal (Gold, Silver, Bronze, or NA, where NA means athlete competed but did not win a medal. See guidance in Data Preparation for how to deal with the NA values) olympic_countries file Country Olympic Code (abbreviation of country, compatible with Country in other files) Making Copy of Report Page, From One File to Another You will likely be partly working independently on your own files, and then seeking to combine (PBI Desktop does not have “live” sharing). This will help. You can copy one report page from one file to another using the following steps: Files must have the same data source(s). Sources here are the three CSV files. Add a blank page in your target PBIX file Go to your source PBIX file, click on the page you want to copy, and with nothing selected on the report page hit CTRL + A (select all). Then hit CTRL + C (copy). Go to your target report file and on the blank page, hit CTRL+V See https://community.powerbi.com/t5/Desktop/Copy-report-page-from-PBIX-to-another- Notes and Tips PBI does not have a direct way to allow multiple people to edit the file simultaneously. You may want to keep your PBIX in a cloud storage folder and make sure everyone has read/write access to that folder, to better maintain version control. You will still need to update the Data Source settings when opening the PBIX file on another computer. Olympics Data Preparation and Dashboard Page 5 of 6See Answer
  • Q14: ADMN 873, Data Management & Visualization Project Instructions PROJECT GOALS The goal of this project is to allow students to apply the concepts that they learn during the course to a problem (preferably related to business/economics but not essential) that they find important and interesting. During the project students will collect data from several sources, wrangle/clean it, relate the data sets together to the extent possible, design a means to store it, and conduct analyses and visualizations to develop insights. PROJECT TASKS 1. Form Your Groups. Before starting the project, read your classmates' self-introductions on Canvas, then identify those individuals who you believe have interesting and valuable ideas and work experiences from the standpoint of a project you would like to work on. Contact them and find out if they are interested in working with you on this project. Each group should have 4±1 members (this will depend on course enrollment). Groups must be formed by the date indicated on Canvas. 2. Define Your Problem: Research Question Identification/Problem Definition. Identify an interesting area you wish to investigate. Develop some questions you would like to be able to address. Identify several data sources that will permit you to address your research questions. 3. Submit Project Proposal. Submit your project description, including data sources, on Canvas. 4. Identify Data Sources & Collect Data. Identify Potential Data Sources. The data that you use in this project will be drawn from various electronic sources including databases (broadly defined), web pages, social media websites, etc. You must have at least 3 different sources of data and one of the data sources must be from a social media website (e.g. Twitter, Reddit, Facebook, LinkedIn, etc.). At least one of your datasets must have at least 1000 records. You may end up with three or more separate, but related data sets. This is OK. If possible, try to relate the dataset together based on a common field (which may be present in the original data, or that you add to the datasets). 5. Clean & Organize Your Data. Once you have collected your dataset you must clean and organize I it to prepare for analysis. Use R, Excel (functions/commands or Power Query), and/or Power BI to organize and clean your data. As part of your submission, include a detailed description of your data cleansing process (usually a bulleted list of steps is good for this). 6. Analyze Your Data. Use R, Excel, and/or Power BI to do Exploratory Data Analysis (EDA). Perform descriptive analytics and gain insights into your research question. This could include conducting some basic statistical analysis including examining the data distribution, computing means, medians, standard deviation, correlations, regression analysis etc. Note that through EDA it is common to find errors, omissions, and anomalies in the data. Make note of these and rectify them as appropriate. 7. Visualize Your Data. Use Power BI to visualize some of your interesting findings. Create multiple visualizations and organize them well with dashboard(s). Include documentation of data sources, assumptions, and key process steps within the Power BI file itself. The file should be able to stand independently so that someone could use it to gain insight into the issue you addressed. While your visualizations should be guided by your research questions, you should seek to create a tool that someone can use interactively to learn more about the situation. Thinking about different "personas" (perspectives, viewpoints) will help you in this process. PROJECT DELIVERABLES There are four main deliverables from this project: 1) project report; 2) technical work product, 2) project presentation and 3) project evaluations of others' projects. 1. Project Report: Your final project report should document each of the tasks outlined above. Please include the sections listed below. The report should be able to stand independently from your work product. That is, someone may only read the report, so include sufficient detail, summaries, and visualizations within the report itself. The report should be professional quality, as one may find in an industry publication, and written in the 3rd person. a. Title Page: Title, authors, course, date. b. Abstract (one-paragraph summary of the project and key findings; should be able to stand apart from the rest of the document). C. Problem Definition, Research Questions, and Key Assumptions d. Data Source Identification and Data Description e. Data Wrangling Process f. Data Analysis & Visualization (incorporate visuals directly in report, or in an appendix with appropriate references in the text). g. Conclusions. What are the key take-aways regarding the questions you are investigating, as well as any key insights gained from doing the analysis? h. Limitations. What are the limitations of the analysis? What would be required to remove or reduce the respective limitations? Lessons Learned - What did you do effectively for this project? If you had a chance to redo the project (or had more time), what would you have done differently (lessons learned)? References - References should be presented using the APA style. Appendix - Feel free to include any supplemental materials that do not fall under any of the above headings in the Appendix. i. j. k. 2. Work Product. This comprises the Power BI (PBIX), Excel, and, associated R files (scripts), and data file(s). Include documentation so that someone can use them to assess what you have done and to use what you have done to gain further insight into the issue. If it is not obvious, include a short file that documents what every file submitted is. Include the data files as separate files in their native format (e.g., CSV) or in Excel tables. 3. Project presentation. Each group will prepare a 12-15 minute video presentation. Explain each of the project tasks listed above. Also detail the data wrangling / analysis / visualization lessons learned from the project. Each student in the group is required to present a portion of the final project. Show the visual (e.g., PowerPoint) and the speaker in the video. Upload an MP4 video file or provide a link to the video. Also post the video to the Discussion Board on Canvas. For the presentation, it is important that students: a. be dressed appropriately (business casual), maintain eye contact with the audience (or camera) and seldom refer to notes. b. present materials in a clear, coherent, logical & interesting manner that can be followed easily. C. convey logical and persuasive arguments. d. demonstrate a strong command of materials that they are presenting. e. make effective use of visual aids. Submission: Submit the report (MS Word or PDF), files associated with the Work Product, the PowerPoint file used for the presentation, and links to any Power BI and/or Tableau visualizations that are web-based. Submit the MP4 presentation file to the Discussion Board provided. 4. Evaluation of Other Projects. After projects are submitted, each person will review 2 other projects based on the video presentations. A feedback form will be provided for this.See Answer
  • Q15: Assignment 4 For this assignment you will use Python to analyze two sets of data. This assignment will require use of the Pandas and Matplotlib libraries. When you are done, upload this completed Jupyter Notebook file as both an .ipynb file and a PDF file. Also upload the modified data (CSV) file from Case 1. Use comments in your Python code as appropriate to explain what you are doing at each step (or group of steps). Provide written responses to any questions using the Markdown boxes provided (add more if needed). You can also add additional code boxes if desired. Case 1: Titanic passenger analysis. The CSV file "RMS_Titanic" contains data about each passenger aboard the RMS Titanic when it sank in 1912. Each record includes whether or not the passenger survived (1 = yes); the pclass (ticket class) they were traveling (1st, 2nd, 3rd); their name, sex, and age; and the fare they paid (in US dollars). Use Python for each task (do not perform manual calculations). import pandas as pd import matplotlib.pyplot as plt 1A. Create a list of only the names of all passengers over the age of 54 that survived. What percentage of the total number of passengers over the age of 54 is this? Markdown box 1B. Create two histograms of age on the same chart. One histogram should be for those passengers that survived, and one for those who perished. The first histogram (survived) should have blue bars, and the second histogram should have yellow bars. Both should use 80 bins, have black edges, and have alpha set to 0.5. Add an appropriate title, axis labels, and a color legend. Comment on any similarities and differences that you observe between the two distributions. Markdown box 1C. What is the average fare paid by all passengers? Of only those who survived? Of only those who did not? State your final answers in dollars and cents. Comment on any perceived correlation of fare with survival. Markdown box 1D. Adjust each fare for inflation. That is, convert the fare values from 1912 dollars into 2024 dollars (add a column called "Adjusted Fares". Save the modified data (all columns) to a new file called "updated2024.csv" and upload it with your assignment. An inflation index can be found at https://www.officialdata.org/us/inflation/1912?amount=1 Markdown box Case 2: Data value distribution. The CSV file "Weight_Males" contains the weight (in pounds) of 5000 randomly selected adult males in the United States. Analyze how well this data set meets the conditions of normality. Use Python for each task (do not perform manual calculations). import pandas as pd import matplotlib.pyplot as plt 2A. Create a histogram of the data using 64 bins. The bars should be green with blue edges. Add an appropriate title and axis labels. Describe how normal the distribution appears to be (based on the histogram). Markdown box 2B. Generate a set of descriptive statistics for the data. What do these statistics tell you about well the data meets the definition of normally distributed data? Markdown box 2C. Test the distribution of the data set against the empirical rule by calculating the percentage of data points that are 1, 2, and 3 standard deviations away from the mean. How well does the data seem to meet the empirical rule? Markdown box 2D. Potential outliers in a data set can be defined as data points that are more than 3 standard deviations away from the mean. Calculate how many data points are potential outliers, then create a list of these potential outliers (i.e., the data points themselves). Markdown boxSee Answer
  • Q16: Objective: This is designed to assess your ability to prepare, analyze, and visualize data related to airline reviews. You will work with two datasets: 1. ● Instructions: 2. ● DSCI 5360_06 Data Visualization for Analytics Data Processing and Transformation Assess the datasets for data quality, including completeness, consistency, and accuracy. Cleanse the data by addressing missing values, eliminating duplicates, and correcting any anomalies or inconsistencies. Top12_airlines_reviews contain 191,123 entries of airline reviews. Airline_top12_list includes detailed information on 12 airlines. Transform the data into a format suitable for analysis. This may involve tasks such as normalization, aggregation, or encoding of categorical variables. Data Visualization ● ● Create insightful visualizations that reveal patterns, trends, and insights within the airline review and airline details datasets. Your visualizations should aim to answer specific questions or highlight notable findings related to airline reviews. 3. Visualization and Design Principles Identify and elaborate on the visualization and design principles you employed in creating your data visualizations. These principles can be derived from class discussions or reputable online resources. Proper citations for these sources are required. Discuss why you selected these principles and how they contribute to the effectiveness of your visualizations. Ensure your visualizations are clear and accurate and communicate their intended insights effectively. Submission Guidelines: Present your findings and visualizations directly in this Word document together with the .twbx source file. Include screenshots of your data processing steps, visualizations, and other pertinent outputs or analyses. Each student's submission must be distinct. Identical or wrong submissions will result in a score of zero for those involved. Evaluation Criteria: Your submission will be evaluated based on: ● Accuracy and thoroughness of data processing: Demonstrated skill in cleaning and preparing data for analysis. Creativity and relevance of visualizations: Visualizations should be insightful, well-chosen for the dataset, and effectively highlight key insights. Application of visualization and design principles: Demonstrated understanding and application of principles that improve the clarity and interpretability of your visualizations. Originality: Your submission should reflect your own analysis and insights. 90% and above: Outstanding Performance You have exhibited an excellent understanding of data analytics and visualization principles. Your work demonstrates thorough data processing and insightful analysis and showcases independent thinking and innovative approaches to visualizing complex data. Your application of visualization and design principles is sophisticated, enhancing the interpretability and impact of your findings. This grade signifies that you have exceeded the basic requirements, contributing unique perspectives and advanced analytical skills to your work. 80% -89%: Above Average You have shown a strong grasp of data analytics and visualization, with work that meets most of the objectives effectively. There are minor flaws or areas for improvement, such as slight inaccuracies in data processing, analysis, calculations, opportunities for deeper analysis, or the application of more varied visualization techniques. These issues do not significantly detract from your analysis's overall quality and clarity but indicate areas where further refinement could elevate your work. This grade reflects solid competence and understanding, with room for enhancement in precision and creativity. 70% -79%: Satisfactory Your work indicates a basic understanding of data analytics and visualization, but there are major flaws or missing components that impact the overall effectiveness of your analysis. This may include incomplete data cleaning, significant gaps in analysis, or a lack of clarity in your visualizations. While you have grasped fundamental concepts, there is a need for a more thorough approach and greater attention to detail. This grade suggests that while you have met some objectives, there is considerable room for improvement in both analytical rigor and the application of visualization principles. Below 70%: Needs Improvement This grade indicates that you are still developing your data analytics and visualization skills. Your submission may lack a coherent analysis, exhibit poor data processing practices, or fail to apply basic visualization and design principles effectively. It suggests a need for a more foundational understanding of the subject matter, as well as practice in applying these concepts to real-world data. Consider seeking additional resources, guidance, and practice opportunities to enhance your abilities in these areas. This offers you an opportunity to demonstrate your skills in data analysis and visualization in the context of airline reviews. Approach it with creativity and critical thinking. Good luck! Tasks Task 1 Data preparation: Extract flight route features (departure-destination) and Splitting Columns: Use Tableau calculations to split the flight route into separate columns: Departure and Destination. Ensure these new features accurately represent each flight's start and endpoints. Clean Data: Filter Null Values: Identify and remove records with null or missing values in critical columns like Departure, Destination, or cabin classes. This ensures the integrity of your analysis. Visualize Data: Map Plot: Plot all flights with departure and destination on a map. Use Size to show the number of flights. Use color to show four cabin classes (Business, Economy, First Class, and Premium Economy). Identify Outliers: Missing Values on Map: After plotting, review the map for any areas lacking flight routes which might indicate missing data not previously identified. This could manifest as major airports with significantly fewer connections than expected. Outlier Detection: Use statistical methods or visual inspection to identify outliers in the number of flights in the distribution of cabin classes. These could indicate data quality issues or genuinely interesting trends. Explanations: Take screenshots of your results and explain your process of doing visualizations and your findings. Cite any references, if any. Task 2 Data preparation: Ensure Relevant Columns: Your dataset should include, but not be limited to, columns such as Review Text, Overall Rating, and Aspect Ratings. Add two new binary columns: COVID-19 Mention (Yes/No) and Refund Mention (Yes/No). COVID-19 Keyword Identification: Tableau calculation to scan Review Text for COVID-related keywords (e.g., pandemic, COVID, coronavirus, virus). This can be achieved through regular expressions or keyword matches. Classification: Assign a "Yes" value to the COVID-19 Mention column for reviews containing any of the identified keywords; otherwise, mark as "No." Refund Keyword Identification: Similarly, identify reviews mentioning refunds or cancellations by searching for relevant keywords (e.g., refund, reimburse, cancellation). Classification: Update the Refund Mention column based on the presence of these keywords, marking it "Yes" for reviews that discuss refunds or cancellations and "No" for those that do not. Visualizations: Overall and Aspect Ratings Comparison: Use a combination of bar charts and box plots to compare overall ratings and aspect ratings (such as Cleanliness, Food & Beverage, Value, and Service) between reviews with and without mentions of COVID-19 and Refunds. Bar Charts: Show average ratings for each category, with separate bars for COVID-19 mentions and refund mentions. Use different colors to distinguish between the two. Box Plots: Provide a distribution view of ratings, which can help identify patterns, outliers, and the spread of ratings in each category. Color Coding: Apply intuitive color coding to your visualizations to enhance readability. For instance, use green for positive outcomes (e.g., reviews without COVID-19 mentions showing higher satisfaction) and red for negative outcomes (e.g., lower ratings in reviews mentioning refunds). Explanations: Take screenshots of your results and explain your process of doing visualizations and your findings. Cite any references, if any. Task 3 Data preparation: Date Segmentation: Ensure your dataset includes a 'Review Date' field. Use this to categorize reviews into 'Pre-Pandemic' (before March 11, 2020) and 'During Pandemic' (from March 11, 2020, onwards). Aspect Ratings: Confirm that your dataset includes ratings for eight aspects of the airline experience, such as Cleanliness, Service, Seat Comfort, and Value. Visualizations: Use any charts you choose to show the trend of average aspect ratings over time, with separate lines for each aspect. This can help identify any significant changes in ratings before and during the pandemic. A bar chart or grouped bar chart can effectively compare the average aspect ratings between the pre-pandemic and during-pandemic periods. Consider using a monthly or quarterly granularity to smooth out short-term fluctuations and better visualize long-term trends. Explanations: Take screenshots of your results and explain your process of doing visualizations and your findings. Cite any references, if any. Task 4 Variable Selection: Choose two variables relevant to your research question or business problem. For instance, if you analyze retail data, you might examine the correlation between advertising spending and sales revenue. Ensure both variables are quantitative, as correlation analysis requires numerical data to compute the relationship strength and direction. Data Preparation: Make sure your dataset is clean, with no missing values or outliers that could skew the results. Use data cleaning techniques to prepare your dataset for analysis. Consider normalizing the data if the variables are on very different scales or if one variable significantly varies in magnitude compared to the other. Correlation Analysis: Use Tableau or any statistical tools to calculate the Pearson correlation coefficient if the data is normally distributed. This will give you a value between -1 and 1, indicating the strength and direction of the relationship. Visualizations: A scatter plot is the most direct way to visualize the relationship between two quantitative variables. Plot one variable on the x-axis and the other on the y-axis. Add a trend line to the scatter plot to visualize the direction and strength of the relationship. Most visualization software, including Tableau, can calculate and display this automatically. Explanations: Take screenshots of your results and explain your process of doing visualizations and your findings. Cite any references, if any. Task 5 Identify areas for exploration: Review Existing Analysis: Review your current findings and identify gaps or areas that might benefit from further exploration. Industry Trends: Investigate recent trends or challenges in your project's domain. For instance, if your project is about e-commerce sales, you might explore consumer behavior changes due to external factors like economic shifts or seasonal trends. Stakeholder Interests: Consider the interests of stakeholders or potential users of your dashboard. What additional information could help them make informed decisions? Propose new questions: Based on the identified areas for exploration, propose new questions that your dashboard could answer. Here are examples based on various domains: Retail Sales: How do sales trends vary by region, and what products are most popular in each region? Healthcare: What are the trends in patient satisfaction scores across different departments, and how do they correlate with staff levels? Education: How does student performance vary across subjects, and what is the correlation between attendance and performance? E-commerce: What are the patterns in customer acquisition costs over time, and how do these costs relate to customer lifetime value? Dashboard design: Multiple Visualizations: Design your dashboard to include various types of visualizations that together answer the new questions. For example, use line charts for trend analysis, bar charts for comparisons, and scatter plots for correlations. Interactivity: Implement filters, selectors, and hover-over details to allow users to interact with the dashboard and explore the data in-depth. This could include filtering by time period, geographical area, or other relevant dimensions. Logical Layout: Organize the dashboard logically, grouping related visualizations near each other and ordering the visualizations to guide the viewer through your analysis. Highlight Key Insights: Use the dashboard to draw attention to the most important findings in relation to the new questions. This could be through annotated trends, highlighted outliers, or summary statistics. Explanations: Take screenshots of your results and explain your process of doing visualizations and dashboard and your findings. Cite any references, if any./nSee Answer
  • Q17: Your next assignment is to make a dashboard for Lyft. I'm giving you two days of data, one for February 28th 2023 and one for March 1st 2023. Your assignment is to make a dashboard that the person who manages the city of Boston for Lyft will review every morning at 8:00 am. The manager of the city of Boston for Lyft is your client. You can expect this will be consumed by your client on March 2nd, and then again on March 3rd, again on March 4th, etc. I'm really specifying this because I don't want annotations in the dashboard unless those annotations will dynamically update in the future, and I don't want dashboard titles to be specific to these two days unless those dashboard titles dynamically update as well. As a general thing to remember, this is a dashboard, not an infographic. Your client is aware that there was a Red Sox spring training game on 3/1/2023 - so that won't be a big surprise in and of itself. She will be interested to know whether there were adequate rides delivered to people during the game. Generally, Lyft has a goal of 95% of all sessions to end in a ride. Usually, sessions don't end in a ride because people are "multi-apping" and call a Uber instead if the Uber is cheaper or closer. For nights like 3/1/2023, particularly in Fenway where the game was, Lyft has an internal goal of achieving 90% of all sessions ending in a ride. Lyft also has a goal that the time to pickup for each ride is less than 5 minutes, and the average number of rides that use surge in a given hour is less than 2%. Price is based on the type of car, the distance of the ride, and whether their was a surge. The manager is aware of the basic formula around this, so I would not make graphs that show things like "Lyft Lux rides make more money than normal Lyft rides, on average." because that is basically true by definition. Lyft has a goal of earning $100,000 per "regular" day and $200,000 on nights that MLB games occur. Lyft has a goal that at least 8,000 unique drivers give a ride each day. In general, Lyft does NOT like surges. It considers surges to be a failure of their platform to not provide drivers where they are needed. Even though surges create a one-time revenue boost, Lyft basically believes that the bad experience of paying a significantly higher cost for a ride outweighs the temporary increase in revenue. I'd encourage you to think about other KPIs Lyft might care about and to visualize those as well. There are more people being picked up from Fenway than are being dropped off at Fenway - I made it this way because I assumed people could get to Fenway from work by walking there or taking the T, but they'd want to Lyft home because it would be cold walking or taking the T home. All you have to submit is a .twbx file. The dashboard should be your standard 1,000 wide by 800 tall dashboard. If you use shapes or colors to indicate not meeting goals then I would suggest making the dashboard so that the shapes or colors change if the goal becomes met.See Answer
  • Q18: SUSS SINGAPORE UNIVERSITY OF SOCIAL SCIENCES ANL201 End-of-Course Assessment - January Semester 2024 Data Visualisation for Business INSTRUCTIONS TO STUDENTS: 1. This End-of-Course Assessment paper comprises 4 pages (including the cover page). 2. You are to include the following particulars in your submission: Course Code, Title of the ECA, SUSS PI No., Your Name, and Submission Date. 3. Late submission will be subjected to the marks deduction scheme. Please refer to the Student Handbook for details. ANL201 Copyright © 2024 Singapore University of Social Sciences (SUSS) ECA January Semester 2024 Page 1 of 4 ECA Submission Guidelines Please follow the submission instructions stated below: A-What Must Be Submitted. You are required to submit the following ONE (1) item for marking and grading: • A Report. B-Submission Deadline • The ONE (1) item of Report is to be submitted by 12 noon on the submission deadline. • You are allowed multiple submissions till the cut-off date for each of the ONE (1) item. • Late submission of any of the ONE (1) item will be subjected to mark- deduction scheme by the University. Please refer to Section 5.2 Para 2.4 of the Student Handbook. C-How the (1) Item Should Be Submitted • The Report: submit online to Canvas via TurnItIn (for plagiarism detection) • Avoid using a public WiFi connection for submitting large video files. If you are using public wireless (WiFi) connection (e.g. SG Wireless at public areas), you might encounter a break in the connection when sending large files. D- Additional guidelines on file formatting are given as follows: 1. Report • Please ensure that your Microsoft Word document is generated by Microsoft Word 2016 or higher. • The report must be saved in .docx format. E-Please be Aware of the Following: Submission in hardcopy or any other means not given in the above guidelines will not be accepted. You do not need to submit any other forms or cover sheets (e.g. form ET3) with your ECA. You are reminded that electronic transmission is not immediate. The network traffic may be particularly heavy on the date of submission deadline and connections to the system cannot be guaranteed. Hence, you are advised to submit your work early. Canvas will allow you to submit your work late but your work will be subjected to the mark-deduction scheme. You should therefore not jeopardise your course result by submitting your ECA at the last minute. It is your responsibility to check and ensure that your files are successfully submitted to Canvas. ANL201 Copyright © 2024 Singapore University of Social Sciences (SUSS) ECA January Semester 2024 Page 2 of 4 F-Plagiarism and Collusion Plagiarism and collusion are forms of cheating and are not acceptable in any form in a student's work, including this ECA. Plagiarism and collusion are taking work done by others or work done together with others respectively and passing it off as your own. You can avoid plagiarism by giving appropriate references when you use other people's ideas, words or pictures (including diagrams). Refer to the APA Manual if you need reminding about quoting and referencing. You can avoid collusion by ensuring that your submission is based on your own individual effort. The electronic submission of your ECA will be screened by plagiarism detection software. For more information about plagiarism and collusion, you should refer to the Student Handbook (Section 5.2.1.3). You are reminded that SUSS takes a tough stance against plagiarism or collusion. Serious cases will normally result in the student being referred to SUSS's Student Disciplinary Group. For other cases, significant mark penalties or expulsion from the course will be imposed. G-Use of Generative AI Tools (Allowed) The use of generative AI tools is allowed for this assignment. • You are expected to provide proper attribution if you use generative AI tools while completing the assignment, including appropriate and discipline- specific citation, a table detailing the name of the AI tool used, the approach to using the tool (e.g. what prompts were used), the full output provided by the tool, and which part of the output was adapted for the assignment; • To take note of section 3, paragraph 3.2 and section 5.2, paragraph 24.1 (Viva Voce) of the Student Handbook; • The University has the right to exercise the viva voce option to determine the authorship of a student's submission should there be reasonable grounds to suspect that the submission may not be fully the student's own work. • For more details on academic integrity and guidance on responsible use of generative AI tools in assignments, please refer to the TLC website for more details; • The University will continue to review the use of generative AI tools based on feedback and in light of developments in AI and related technologies. ANL201 Copyright © 2024 Singapore University of Social Sciences (SUSS) ECA January Semester 2024 Page 3 of 4 (Full marks: 100) Section A (100 marks) Answer all questions in this section. You are given a dataset pertaining to the tasks assigned for this ECA. The name of the dataset is <Electric_Vehicle_Population_Data_usa.csv>. This dataset is posted with the ECA question paper on the Canvas system. Question 1 For the given dataset, produce a table that identifies and justifies the data type (nominal, ordinal, interval, or ratio) for each variable; the table should present the summary statistics (mean, median, number of observations, mode, minimum and maximum) for all the variables. Present the answer in tabular format. (20 marks) Question 2 Inspect the data for quality issues and prepare the data using appropriate treatment methods. Explain the data quality issues identified and treatment methods applied with the aid of suitable screenshots. (Maximum word count: 250 words) (30 marks) Question 3 For the treated dataset, develop four charts to visualise and explain the key patterns found in the data. Use each of the following chart types or features at least once: • Reference line • Parameters Provide screenshots of the charts in your report. (Maximum word count: 300 words). Question 4 (25 marks) Using the charts created in Question 3, develop a business performance dashboard. Explain how your dashboard follows the Dashboard Design Principles. Provide a screenshot of your dashboard in the report. Set up two (2) relevant Actions (of different types) for advanced dashboard navigation so that users can perform some of the following tasks: filtering, highlighting, drilling down or navigating to an external HTTP link. Provide screenshots of the Action settings. (Maximum word count: 300 words) END OF ECA PAPER ANL201 Copyright © 2024 Singapore University of Social Sciences (SUSS) ECA January Semester 2024 (25 marks) Page 4 of 4See Answer
  • Q19: (Full marks: 100) Section A (100 marks) Answer all questions in this section. You are given a dataset pertaining to the tasks assigned for this ECA. The name of the dataset is <Electric_Vehicle_Population_Data_usa.csv>. This dataset is posted with the ECA question paper on the Canvas system. Question 1 For the given dataset, produce a table that identifies and justifies the data type (nominal, ordinal, interval, or ratio) for each variable; the table should present the summary statistics (mean, median, number of observations, mode, minimum and maximum) for all the variables. Present the answer in tabular format. (20 marks) Question 2 Inspect the data for quality issues and prepare the data using appropriate treatment methods. Explain the data quality issues identified and treatment methods applied with the aid of suitable screenshots. (Maximum word count: 250 words) (30 marks) Question 3 For the treated dataset, develop four charts to visualise and explain the key patterns found in the data. Use each of the following chart types or features at least once: • Reference line • Parameters Provide screenshots of the charts in your report. (Maximum word count: 300 words). Question 4 (25 marks) Using the charts created in Question 3, develop a business performance dashboard. Explain how your dashboard follows the Dashboard Design Principles. Provide a screenshot of your dashboard in the report. Set up two (2) relevant Actions (of different types) for advanced dashboard navigation so that users can perform some of the following tasks: filtering, highlighting, drilling down or navigating to an external HTTP link. Provide screenshots of the Action settings. (Maximum word count: 300 words) - END OF ECA PAPER (25 marks)See Answer
  • Q20:INSTRUCTIONS Tasks: Data Exploration and Cleaning: Import the 2 datasets to excel and examine the structure. Clean the data if there are any inconsistencies or missing values. Then once the data is clean, upload each to tableau. Visualization: Create a dashboard with the following visualizations: Sales trends over time, broken down by product type. Comparison of actual vs. budgeted sales and profits. Profit margins by state and market size. In-Depth Analysis: Analyze how different factors (market size, state, product type) impact sales and profits. Identify the best and worst-performing products. Insights and Recommendations: Develop insights on how to increase profitability. Suggest strategies to improve underperforming products or markets. Presentation: Summarize your findings in a clear, concise presentation within Tableau.See Answer
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