Analyze the below Walmart business case and the sample customer royalty card dataset, and then write a business report like your individual assignment one (A1) for your Walmart CEO to address the following 5 business analytics questions with equal grading weight

1. What is data strategy which Walmart should adapt? What are data privacy issues which this company should be concerned about? 

2. Given the above sample customer royalty card dataset, please apply the CRISP-DM process to do a data analyst on this data to tackle this company's business problems (please explain each of the 6 2 steps in detail related to the Walmart retailing business). And then, please specify 8 steps which you used for your individual assignment two (A2) to do Data Visualization using Tableau or PowerBI; 

3. How can you use K-means Clustering technique to do the Walmart customer profiling based on this sample dataset and what kinds of insights you are planning to find? Please recall your team project experiences and the professor’s lecture & live demo in class and try to explain as much detail as you can for this Walmart business case (no actual data analytics is required!). 

4. How can you use one or more Classification techniques such as Linear Regression, Logistics Regression, Support Vector Machine, Naive Bayes, Decision Tree, Time Series Forecast, learned from this course to do data analytics on the sample dataset and what kinds of trends or patterns you are going to find? Please recall your team project experiences and the professor’s lecture & live demo in class and try to explain as much detail as you can for this Walmart business case (no actual data analytics is required!). 

5. Please Conclude and make a final recommendation of Action Plan for Walmart CEO to take based on the above business analytics work, aimed to improve Walmart’s data-driven decision making for better business performance and gaining competitive advantages in the global retailing marketplace.

 

Customer Royality Card Dataset (Example for Final Exam Purpose) Total Row: 200, Column: 5 CustomerID Gender Age Annual Income Spending Score (1-100) 1 Male 19 15 39 2 Male 21 15 81 3 Female 20 16 6. 4 Female 23 16 77 5 Female 6 Female 7 Female 8 Female 9 Male 31 17 40 22 17 76 35 18 6. 23 18 94 64 19 10 Female 30 19 72 11 Male 67 19 14 12 Female 35 19 99