David

Data scientist🧉

[LEANDROMV1999 @Ultranormalidad]

Este video me da escalofríos porque es REAL.

[Wall Street Wolverine @wallstwolverine]

Milei cuenta por qué se lanzó a la política: “Me dijeron, si no te gusta, armá un partido y gana las elecciones...”

[David @David_Silvera21]

Me parece que tengo un poco de Adobe en mis impuestos.....

#afip #impuestos #choreo #robo

[David @David_Silvera21]

@thatroblennon Its amazing buddy!
thanks so much 👏

[David @David_Silvera21]

Thanks you for read the thread, if you have some question i will be glad of reply to you ASAP ;)

#DataCleaningTips #DataScience #DataPreprocessing #DataCleaningStrategies #DataWrangling #DataCleaningTricks #DataCleaningBestPractices #DataCleaningProcess

[David @David_Silvera21]

(1/12) Data cleaning is often the most tedious part of data science, but it's also one of the most important. It's the process of ensuring that your data is accurate, consistent, and in a format that's ready for analysis. Here are some tips I've learned to make it more efficient:

[David @David_Silvera21]

(12/12) Remember that data cleaning is an ongoing process. As you continue to work with your data, you may discover new issues that need to be addressed. Stay vigilant and keep your data clean!

[David @David_Silvera21]

(11/12) Don't be afraid to ask for help. Data cleaning can be a complex process and it's always good to have another pair of eyes on it.

[David @David_Silvera21]

(10/12) Keep a backup copy of the original data. This will be helpful if something goes wrong during the cleaning process.

[David @David_Silvera21]

(9/12) Use the right data type for each column. This can help with data analysis and visualization later on.

[David @David_Silvera21]

(8/12) Automate repetitive tasks to save time using functions

[David @David_Silvera21]

(7/12) Test your cleaning process to validate that the final output is what you expect it to be.

[David @David_Silvera21]

(6/12) Document your cleaning process. This will help you remember what you did and make it easier for others to understand.

[David @David_Silvera21]

(5/12) Understand the data structure before cleaning. This will help you identify any issues or errors that need to be addressed.

[David @David_Silvera21]

(4/12) Make use of regular expressions to search and replace specific patterns in your data. This can be a powerful way to clean and transform your data.
https://t.co/3N06UimeuY

[David @David_Silvera21]

(3/12) Develop a consistent naming convention for your data. This will make it easier to refer to columns and variables later on in your analysis: Google Python Style Guide - another popular style guide that includes recommendations for naming conventions. https://t.co/0Qi7nbqi4I

[David @David_Silvera21]

(2/12) Use existing tools and libraries such as Pandas to handle and clean data in a standardized way and Numpy, a powerful library for working with arrays and matrices of numerical data, These tools can save you a lot of time and effort when dealing with large datasets.

[David @David_Silvera21]

@SteveNouri 👋

[David @David_Silvera21]

Notion + AI = magic 🪄 Join me in the alpha waitlist! https://t.co/AyVbJGEyWG