opic

You are acting as a Business Data Analyst tasked with presenting findings to senior management. Your role is to analyse data, execute regression analysis, and present results in a professional, management-friendly presentation.

Objective

To develop and present actionable business insights through regression analysis, supported by a clear business context and well-defined research questions.

Assessment Structure

Section 1: Business Context

• Describe the business environment in which your dataset is relevant.
• Provide background on the industry, company, or specific problem the dataset addresses.
• Clearly explain why this analysis matters to management/stakeholders.

Section 2: Research Questions

• Formulate clear and relevant research questions based on the dataset.
• Justify the importance of these questions for business decision-making.

Section 3: Data Analysis

• Execute regression analysis on the dataset to address your research questions.
• Include and document regression tables (coefficients, p-values, R, etc.).
• Where appropriate, include other statistical outputs that support your analysis.
• Clearly interpret the regression output in plain language for a management audience, along with statistical evidence.

Section 4: Business Insights

• Translate the regression results into clear, actionable insights.
• Explain what the results mean in the context of the business problem.
• Highlight any significant relationships or predictors identified.

Section 5: Recommendations

• Suggest specific business actions or strategies based on your analysis.
• Explain the expected business impact of implementing these recommendations.

Deliverables

• PowerPoint Slides designed for a management audience:
  • Concise, visual, and professional.
  • Include relevant charts/tables in your slides. However, include your regression tables in the appendix.
  • Minimise technical jargon; focus on clarity and implications.
  • A written report and an actual presentation are not required.
• Dataset Source include a reference or link to your dataset.

Notes for Students

• Select a dataset that allows for meaningful regression analysis.
• Your presentation should tell a story: start with the business need, explain your analysis approach, present results, and end with recommendations.
• Avoid clutter keep slides clear, visual, and management-focused.

Referencing and Citation:

As part of your academic responsibilities, all submitted work must adhere to the APA 7th edition guidelines for proper referencing. Please refer to to verify citation and formatting rules before submitting your assignments.

Additionally, make sure to utilize the Coach to review your papers before submission (not after). Instructions for this can be found on the Program Immersion Course page.

AI Use:

You may use AI tools for this assignment as per the myHult . All AI usage must be acknowledged as per the policys citation framework. If unsure about a tool, consult the professor. Unacknowledged AI work will be treated as an academic integrity violation and referred to the Academic Integrity Committee.

note that submit as PowerPoint ; no strict slide cap; include Excel calculation tables in an appendix; focus on regression analysis in Excel

so what need powerpoint and excel sheet

What to Upload (Required)

Upload the below 2 items:

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1? Written File (PDF or Word, max 2 pages)

Must include:

• Part A: Questions + Hypotheses
• Part C: Executive Summary

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2? Tableau Workbook (.twbx)

File name must include your name
(e.g., Firstname_Lastname_Week4_Lab.twbx)

The extension MUST be .twbx

The workbook must contain:

• Exactly 3 worksheets
• Correct worksheet names:
  • 1_Category_to_Subcategory
  • 2_Sales_and_Profit_Trend
  • 3_Profit_Map
• A caption on each worksheet

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Submission Checklist

Before submitting, confirm:

• Written file is PDF or Word ( 2 pages)
• Tableau file is .twbx and includes your name
• Exactly 3 worksheets
• Worksheet names match exactly
• Each worksheet has a caption
• No numbers are invented

All information in the attached file

Data Analytics is the process of examining raw data to uncover patterns, draw meaningful insights and support better decision making. It helps individuals and businesses understand past performance, monitor current trends and predict future outcomes.. Features of Data Analytics are

• Insight Generation: Helps identify trends, patterns and anomalies in data to make informed decisions.
• Predictive Capabilities: Uses historical and current data to forecast future outcomes and opportunities.
• Data Management: Involves building systems and tools to efficiently handle, process and analyze large volumes of data

Overview:

A national company has been receiving customer complaints across a wide variety of products and services. To improve customer satisfaction and operational efficiency, the company needs an in-depth analysis of these complaints to identify patterns and actionable insights. Your task is to assist the company by creating an interactive Tableau dashboard and providing strategic recommendations based on your findings. Demonstrate original critical thinking and synthesis of ideas; reliance on AI-generated analyses is inappropriate and does not fulfill the requirements for graduate-level scholarship.

Background:

The company has provided a dataset containing information about customer complaints. This dataset includes various details such as the products involved, the nature of the complaints, how complaints were submitted, and the resolution provided. Your analysis will focus on uncovering trends, understanding customer priorities, and evaluating the companys responsiveness.

Dataset Description:

The dataset includes the following columns:

• Customer ID: A unique identifier for each customer.
• Product: The specific product associated with the complaint.
• Product Category: The broader category of the product.
• Issue: Description of the issue reported by the customer.
• Consumer Disputed?: Indicates if the consumer disputed the resolution (Yes, No, or Pending).
• Date Received: The date the complaint was received.
• Date Submitted: The date the complaint was submitted by the consumer.
• Submitted via: The channel through which the complaint was submitted (e.g., Website, Email, Phone).
• Tags: Priority level or special conditions (e.g., High Priority, Escalated).
• Timely Response?: Whether the company provided a timely response (Yes or No).
• ZIP Code: Customers ZIP code.
• Number of Complaints: Count of complaints submitted by the customer.
• State: The state where the customer resides.
• Company Response to Consumer: Outcome or resolution provided by the company.

Objectives:

Your goal is to analyze the dataset and provide actionable insights through an interactive Tableau dashboard. The final deliverable should help the company achieve the following:

• Identify trends and patterns in customer complaints.
• Understand which products and categories receive the most complaints.
• Evaluate the effectiveness of the company’s responsiveness and resolutions.
• Propose recommendations to enhance customer satisfaction.

Task:

Data Cleaning and Preparation:

• Ensure data consistency and accuracy.
• Handle missing values and correct any data inconsistencies.
• Derive additional columns if needed.

Dashboard Design:

• Design a dashboard that is clear, visually appealing, and easy to interpret, modeled after the example dashboard shown in the provided video.
• Use appropriate chart types, colors, and labels to enhance readability.

Analysis and Insights:

• Analyze the data and provide insights based on the visualizations.
• Identify trends, patterns, and any notable observations from the data.

Presentation:

• Prepare a brief report or presentation summarizing the findings.
• Include a 20 – 25 minute presentation video where you walk through the workings of the dashboard and a brief summary of the analysis. The presentation should cover:
• Overview of the Dashboard: Demonstrate the dashboard’s interactive features, functionality, and key insights.
• Data Sources and Preparation: Explain the data cleaning and preparation steps and highlight key aspects of your analysis.
• Detailed Walkthrough: Go through each visualization, explaining what it shows and why it is important.
• Key Insights and Observations: Summarize the main findings and insights from the data.
• Technical Aspects: Briefly discuss any technical challenges or interesting techniques used in creating the dashboard.

Supporting files: * Tableau dashboard file* Brief, 1-2 page executive report summarizing the findings

Submission: (submit all THREE items)

1. Tableau dashboard file

2. Prepare a brief executive report (1-2 pages) summarizing the findings.

3. Submit a video file demonstrating the dashboard’s interactive features, functionality, and key insights.

Any dashboard generated using AI will be marked zero.

Evaluation Criteria:

• Accuracy of data cleaning and preparation.
• Completeness and clarity of visualizations.
• Creativity and effectiveness of the dashboard design.
• Depth of analysis and quality of insights.
• Overall presentation and communication, including the effectiveness of the recorded presentation video.

Regression is an analytical technique used to make a relationship between input variables and continuous outcome variables.

A sample of data on cars is provided for this assignment. The file contains data on sales of used cars with 1436 records containing details. The attributes of the data include Age (Age in years), KM (Accumulated Kilometers on the odometer), HP (horsepower), CC (Cylinder Volume in cubic centimeters), Doors (Number of doors), Weight (Weight in Kilograms), and Price (Price of Cars). Run a multiple regression with the outcome variable Price and the predictor variables Age, KM, HP, CC, Doors, and Weight. (Using Python, Jupiter Notebook)

Before performing the analysis.

1. Perform descriptive analysis.
2. Perform correlational analysis and describe the analysis.
3. Apply standardization to the data.
4. Perform correlational analysis.
5. Split the data into a training set and a testing set of the data.
6. Perform multiple regression on the training set!
7. Perform
8. Explain the linear regression coefficient and intercept.
9. Specify the linear regression equation.
10. Perform model evaluation Mean Squared Error (MSE)

Include a brief description of each step and the screen print of your Python codes in your paper.

For this week’s discussion, you will discuss the following:

1. Name three types of Interactive Actions that you can add in a Dashboard. (Tableau)
2. What are some of the uses of these Actions?

Here is the material.

For this week’s discussion, you will discuss the following:

1. Name three types of Interactive Actions that you can add in a Dashboard. (Tableau)
2. What are some of the uses of these Actions?

Here is the material.

Data refers to raw, unorganized facts and figures, such as numbers, text, images, or symbols, that can be processed and analyzed to extract meaningful information

• Data can exist in a raw form (unorganized) or processed form (organized and meaningful).
• A database is a structured collection of data designed for efficient storage, retrieval and manipulation.
• It serves as a centralized repository, allowing data to be accessed, managed, and updated by multiple users or applications.
• A high-performing database is vital for any organization, supporting operations, customer interactions and systems like digital libraries, reservations, and inventory management. Databases are essential because they:
• • Scale efficiently to handle massive volumes of data.
  • Ensure data integrity through built-in rules and constraints.
  • Protect data with secure access controls and compliance support.
  • Enable analytics by identifying trends and guiding informed business decisions.

In this assignment you will 1) research the total debt D and 2) calculate the weighted yield to maturity R_D for your assigned corporation. To find R_D we must create a table in Excel. Note: If your corporation data has more than ten bonds, just choose any ten for your calculations in the Excel table. You will use your calculated R_D to complete the WACC calculation due next week.

STARBUCKS is my corporation

Click on the Symbol of two of your bonds. In a separate word document answer the following questions (feel free to copy/paste these into your word document):

1. Bond Maturity:
   1. What maturity date is farthest into the future? Approximately how many years (or months) from now?
   2. What maturity date is the next one to expire? Approximately how many years (or months) from now?
   3. Refer to Figure 7.2 on page 206 in the text. Of the two bonds above, which bond has the most interest rate risk?
2. The Call Provision:
   1. Are either of the bonds Callable?
   2. Click on the ^ in the Callable row. For callable bonds, is it important to consider your yield-to-call? Why?
   3. Refer to “The Call Provision” on page 215-216 of the text. True or False: In a “make-whole” call provision, the call price is higher when interest rates are lower and vice versa.
3. Total Debt:
   1. Since the debt D calculated in your excel table is only a sampling, you must find the total corporate debt elsewhere. Do a Google search for “Long Term Debt for ______” using your corporation name. You may also use any other source to find the Long Term Debt. Enter the web link and the Long Term Debt value here. You will use this total debt D to complete the WACC calculation due next week.
   2. You will see your company bond data in a table like the one below. Note that you must use the bottom scroll bar to see all the data. If your computer does not show the columns below, simply click on Columns to apply any missing columns. Make note of the Quantity, Price, and Yield data that appears. You will enter this data in your spreadsheet. You must click on the Symbol to get the answers required for the written part of this assignment.
      

   3. Read the Case Study detailing the creation of a baseball stadium. Define a list of the steps needed to build the stadium using the case study and add as much detail as needed. Be specific in your assumptions (days, hours worked, etc). With this information documented, create a Gantt chart and the critical path of the project. Your assignment should include a Gantt chart, clear identification of the Critical path, and a written paragraph defining your project assumptions and answering the questions presented in the case study. This goes with the gnat chart document assignment.

      The objective of ‘crashing’ is to reduce the time of a project at an acceptable additional cost. For this assignment, determine all possible paths use the information contained in the chart. Compress one time unit per move using the least cost method. Assume the total indirect cost for the project is $700 and there is a savings of $50 per time unit reduced. Record the total direct, indirect, and project costs for each duration.Show your work including your original options and any ‘crashing’ activity in detail.Questions to answer: this goes with the other document attached crash time exercise

      1. What is the optimum cost-time schedule for the project? (in units)
      2. What is the cost?

      ALL ASSIGNMENTS MUST HAVE SEPARATE DOCS