The document discusses a project to scrape notable death information from Wikipedia over the last 40 years, clean the data, and perform analysis. Key tools used include Python for web scraping and importing data into arrays, and Excel for data cleansing, filling in additional details, analyzing similarities, and creating charts. The demonstration and conclusion sections provide an overview of what was accomplished with the project and the business rationale for using a solution with Excel, which is widely accepted and customizable.

1. Cleansing Big
Data
Final Presentation

2. Agenda
• Project Overview
• Tools used
• Use Cases
• Demonstration
• Conclusion

3. The Problem
Our task is to scrape a Wikipedia page
containing notable deaths for the last 40
years, then create meaningful insights
from our analysis.

4. Why Clean
Data?
COMPANIES ARE BECOMING MORE
AND MORE DATA DRIVEN, BUT THE
DATA NEEDS TO BE ACCESSED
SOMEHOW
TOOLS LIKE PYTHON AND R CAN
CAPTURE THIS DATA
TOOLS SUCH AS PYTHON DO NOT
ALWAYS MEET DEMONSTRATED
BUSINESS NEED

5. Web Scraping
(Python)
• Used to scrape the Wikipedia Page
• Import Data into arrays
• Append Arrays together
• Print the data to a CSV

6. Data Cleansing
(Excel)
• Clean the Data
• Fill in additional info (dates, nationalities)
• Perform further analysis
• Chart the similarities in the “Ground Truth”

7. Our Solution Caters to the
Ultimate Business Need
• Excel is Popular with businesses
• Allows for customization
• Can be used for different datasets, especially ones
with dates
• Not as costly, as most businesses have access to
Excel

8. Demonstration

9. Conclusion
• What we accomplished
• Scrape all webpages
• Import the data into excel
• Clean/modify the data in excel using a macro
• Business Use Rationale
• Excel is widely accepted within the business
community
• This will make our project more viable for a
business to use