ANL203: (Analytics for Decision-Making) Identify a single business question that can be addressed through an analysis of the dataset “Airbnb_TMA.xlsx”.

Module / Subject / School:

ANL203: Analytics for Decision-Making

Singapore University of Social Sciences (SUSS)

Requirements: 

Since 2008, Airbnb has been facilitating a transformative travel experience, enabling guests and hosts to explore expanded possibilities and discover a more personalized way of experiencing the world. The dataset “Airbnb_TMA.xlsx” provides valuable insights into the listing activity and metrics of Airbnb accommodations in New York City for the year 2019.

Each entry in the dataset represents detailed information about a specific listing, encompassing essential attributes such as the listing ID, geographical locations, prices, and other relevant data. Additionally, the “Sheet2” within the file serves as a data dictionary.

(a) Identify a single business question that can be addressed through an analysis of the dataset “Airbnb_TMA.xlsx”. Provide a clear description of the pertinent data fields and how these fields can be utilized to address the business question. (Maximum of 150 words for part (a)) (15 marks)

(b) Generate a summary of the dataset “Airbnb_TMA.xlsx” in tabular format. You should include the classification of each data field as nominal, ordinal, interval, or ratio.

Examine individual variables within the dataset and compute relevant summary measures when applicable. (Up to 200 words for part (b)) (15 marks)

(c) Apply data preparation steps to address the data issues and errors in “Airbnb_TMA.xlsx” with explanations and justifications for necessary data transformations. Illustrate the data preparation with relevant example screenshot(s). (Up to 200 words for part (c)) (28 marks)

(d) Employ two (2) graphical charts and one (1) pivot table from “Airbnb_TMA.xlsx” to present the key features of the data variables or to analyse the relationship among the

variables. Please provide a screenshot of each produced chart and pivot table. In your explanation, describe the process of creating the charts and the pivot table and discuss why these visualizations are recommended. (Up to 250 words for part (d)) (22 marks)

The word limit does not apply to visuals or screenshots of data preparation example(s), charts and pivot tables. 20 marks will be allocated to the professional presentation and writing of your TMA report, covering the following aspects:

• Organisation

• Communication of ideas

• Citation and reference

• Grammar

What we score:

76%

Our Writer’s Comment 

This assignment is designed to assess students’ understanding of business practices.

To secure an A+ grade, adhere to these guidelines and make sure your work aligns with the grading criteria:

Question (a): Identifying a Business Question (15 marks)

Here, you’re asked to define a business question that can be addressed using the dataset. Your business question should be specific and tied to the available data.

• Example Business Question: “Which neighbourhoods in New York City have the highest average Airbnb listing prices?”
• Data Fields: Use fields like price, neighbourhood, and room type to answer the question. For instance, the price field gives the listing price, while neighbourhood helps identify geographical trends.

Tip: Be concise but clear. Stick to one business question and explain how specific data fields will help answer it. Use about 140-150 words, so you don’t exceed the word limit.

Question (b): Summary of the Dataset (15 marks)

In this section, you need to provide a classification of each data field (nominal, ordinal, interval, or ratio) and summarize the dataset with relevant measures.

1. Classification Example:
   • Listing ID: Nominal (used for identification, no order or meaningful difference between values)
   • Price: Ratio (has a true zero, and differences between values are meaningful)
   • Neighbourhood: Nominal (categorical variable without inherent order)
2. Summary Measures: For numerical fields like price, compute summary statistics like the mean, median, and standard deviation. For categorical variables like neighbourhood, list the most common categories or the count of listings per neighbourhood.

Tip: Create a simple table to show your classifications and summary measures. This makes your presentation clear and organized.

Question (c): Data Preparation (28 marks)

This part is about cleaning the data to handle errors and inconsistencies. Explain how you would clean up missing or erroneous data.

1. Identify Errors: Check for missing or incorrect values. For example, there might be missing data in the pricefield or unusually high/low prices that don’t make sense.
2. Steps for Cleaning:
   • Imputation: For missing values, explain how you would use mean imputation for prices, or remove records where essential fields are missing.
   • Data Transformation: For instance, if dates are formatted incorrectly, describe how you would standardize them.
3. Screenshots: Include before-and-after screenshots of the dataset to illustrate your data cleaning steps.

Tip: Keep your explanations simple and precise. Focus on the why—why you’re transforming certain data fields, and why it improves the dataset quality.

Question (d): Graphical Charts and Pivot Table (22 marks)

Here, you’ll use two charts and one pivot table to present key findings from the dataset.

1. Choosing Charts:
   • Bar Chart: A bar chart could show the average price per neighbourhood. Explain why this is useful for comparing prices geographically.
   • Pie Chart: A pie chart could visualize the distribution of room types (e.g., private room, entire home) to see what’s most common in the dataset.
2. Pivot Table:
   • Create a pivot table to show average price by neighbourhood and room type. This table provides a breakdown of price patterns across different areas and room categories.
3. Explanations:
   • Describe how you created the charts and pivot table using the data in Excel.
   • Discuss why these visualizations are useful for understanding key relationships in the dataset.

Tip: Make sure to include screenshots of the charts and pivot table, and explain how they help answer the business question you posed in part (a).

Professional Presentation and Writing (20 marks)

This section covers the overall quality of your report. Here’s what you can do to improve:

1. Organization: Ensure that each section is clearly labeled, and the structure flows logically. Use headings and subheadings to make the report easy to navigate.
2. Clarity: Keep your explanations clear and avoid jargon. Ensure each point is concise and contributes to your argument.
3. Grammar and Citations: Proofread for grammar and spelling. Cite any external sources you use and refer to the data dictionary where necessary.

Tip: Use bullet points or numbered lists to break up large sections of text and make your ideas clearer. Tables and charts should be properly labeled and referred to in the text.

General Tips to Improve Your Score:

1. Be Specific: Ensure each answer is focused and relates directly to the question. Avoid unnecessary information.
2. Visual Appeal: Use tables, charts, and screenshots to make your report visually engaging. This can improve how well your ideas are communicated.
3. Justify Your Choices: Whenever you choose a chart, method, or cleaning step, explain why it’s the best option for the dataset and business question.

By refining your approach to each section, organizing your ideas clearly, and using strong visuals, you’ll be well on your way to improving your score!

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