Move from Excel to Python with Pandas Transcripts
Chapter: Welcome to the course
Lecture: Challenges of Excel
0:01 before we go into Python, let's talk a little about some of the challenges with
0:04 Excel. Let's take a look at one really big financial disaster that had excel as
0:10 a part of the problem. Many of you may have heard about the massive trading
0:14 losses that JP Morgan and Chase experienced in 2012.
0:18 In this specific example, JP Morgan and Chase lost over $6 billion.
0:24 There were many contributors to this error,
0:25 but one of the compounding issues was that Excel performed badly is a financial modeling tool
0:30 I'm sure you may have not made an Excel error that cost billions of dollars
0:35 but if you have been around excel enough,
0:37 you have probably seen people try to use it in ways that was not intended for
0:42 Excel errors are not exclusively founding companies.
0:45 The government, not surprisingly, uses excel and makes some of the same mistakes in
0:50 the next example, the British intelligence organization.
0:53 MI5 found, they had mistakenly bugged 1000 the wrong phone numbers due to
0:59 an Excel formatting error in the spreadsheet.
1:02 Maybe you have not seen a financial modeling error,
1:04 but I can almost guarantee that you have seen data issues in your spreadsheets when numbers
1:09 and dates are not stored properly.
1:11 These examples highlight the widespread adoption of Excel and how many meaningful decisions are made based
1:16 on the result. An Excel spreadsheet in your own usage of excel.
1:21 You've probably seen some of these types of errors,
1:23 hopefully as one of the reasons why you're taking this course.