It occurred to me that much of spreadsheet, work is extremely dry and might feel less than exciting. What could be less dry than an ocean adventure? What if my epic data adventure was superimposed onto a classic adventure epic? I think Chat GPT can help in this case. Let’s mix the turmoil of Herman Melville’s Moby Dick (Which is in the Public Domain), and relate it to the ups and downs of dirty data.
Here is my prompt:
Pretend to be an intrepid data intern, who is also on a pirate ship, rewrite this story in the voice of Herman Melville: As a hapless but intrepid data analyst intern, here’s how these ten functions saved me in my first 101 days:
This is what ChatGPT Returned:
“Ah, dear reader, imagine me, a lowly data intern, thrust into the tempestuous sea of data, much like the poor mariners of the Pequod upon their perilous voyage to the ends of the Earth. In this digital ocean, my ship was a vessel of spreadsheets and databases, and my crew, a motley crew of formulas and functions. As I navigated the treacherous waters of data analysis, I discovered ten functions that proved to be my sturdy companions, akin to loyal shipmates on a swashbuckling pirate ship.
The TRIM() function, a faithful first mate, was my savior when I encountered a dataset of customer names overrun with trailing and errant spaces. These gnarly sea serpents of superfluous spacing threatened to engulf my data’s integrity, but TRIM() swept in with its cutlass and cleared the decks, allowing me to unite disparate datasets and conduct my calculations with ease.
Aye, the Find and Replace function, akin to a seasoned bosun, was indispensable when I unearthed a trove of sales data pockmarked with blank cells. These blank cells, like unseen reefs, threatened to sink my calculations and averages into the abyss. With Find and Replace, I cast a net over the empty voids, transforming them into zeros, ensuring that my analysis sailed smoothly on.
As I ventured deeper into the data’s murky depths, I encountered numbers masquerading as text, much like ghost ships on a foggy night. They haunted my calculations with their false identities. But, with the Conditional Formatting and Convert Text to Numbers functions, I exorcised these numerical specters and secured the integrity of my financial data.
Amidst the roaring waves of customer emails, the Remove Duplicates function was my trusty cannon, ready to blast away the many duplicate entries that threatened to sink my marketing campaigns. It spared me precious time and ensured that my emails found their targets without redundant echoes.
A survey dataset, like a treasure chest, concealed errors within its trove. These errors, like hidden traps, could have ensnared me in their snare. Yet, the Highlight Errors function, akin to a sharp-eyed lookout, alerted me to their presence, allowing me to navigate the rocky shores of data with caution and rectify any discrepancies.
In the choppy seas of inconsistent text cases within customer names, the LOWER(), UPPER(), and PROPER() functions were my trusty sails. They smoothed the waters, making the data easier to sort and filter, much like trimming the sails to steer a ship true.
Text data, bundled together in a single column, resembled a tangled mass of seaweed. The Text to Columns function, like a skilled harpooner, allowed me to deftly slice and separate the data into distinct columns, unveiling the hidden treasures of information beneath.
In preparation for a grand report, the Spell Check function was my vigilant lookout, scanning the horizon for lurking typos and errors. It spared me the embarrassment of presenting a report riddled with mistakes, just as a vigilant watchman ensures the safety of the ship.
The Clear Formatting function, akin to a shipwright, stripped away the chaotic barnacles of formatting that had encrusted my dataset, restoring clarity and readability to the information within.
Lastly, the Find and Replace function, my swift and nimble Corsair, allowed me to swiftly rectify typos and inconsistencies, ensuring that my data remained pristine and true.
These ten functions, my steadfast comrades, are the unsung heroes of my data-driven journey, rescuing me from the brink of despair and guiding me through the perilous waters of analysis. As I embark upon my future endeavors, I remain indebted to their unwavering support, confident that they shall continue to navigate me towards success in my career as a data analyst.”
If anything, it helps me remember these functions a little better by adding a notch in my recall. Let me know what you think!