Move from Excel to Python with Pandas Transcripts
Chapter: Welcome to the course
Lecture: Challenges of Excel

Login or purchase this course to watch this video and the rest of the course contents.
0:01 before we go into Python, let's talk a little about some of the challenges with
0:05 Excel. Let's take a look at one really big financial disaster that had excel as
0:11 a part of the problem. Many of you may have heard about the massive trading losses that JP Morgan and Chase experienced in 2012.
0:19 In this specific example, JP Morgan and Chase lost over $6 billion. There were many contributors to this error,
0:26 but one of the compounding issues was that Excel performed badly is a financial modeling tool
0:31 I'm sure you may have not made an Excel error that cost billions of dollars but if you have been around excel enough,
0:38 you have probably seen people try to use it in ways that was not intended for Excel errors are not exclusively founding companies.
0:46 The government, not surprisingly, uses excel and makes some of the same mistakes in the next example, the British intelligence organization.
0:54 MI5 found, they had mistakenly bugged 1000 the wrong phone numbers due to an Excel formatting error in the spreadsheet.
1:03 Maybe you have not seen a financial modeling error, but I can almost guarantee that you have seen data issues in your spreadsheets when numbers
1:10 and dates are not stored properly. These examples highlight the widespread adoption of Excel and how many meaningful decisions are made based
1:17 on the result. An Excel spreadsheet in your own usage of excel. You've probably seen some of these types of errors,
1:24 hopefully as one of the reasons why you're taking this course.

Talk Python's Mastodon Michael Kennedy's Mastodon