Best Data Cleaning Quotes

"Data preparation and data cleaning... more than 60% of the work that you'll do is like data preparation and data cleaning."

"The project... follows the structure of a data analysis project... you start with asking the right questions, you prepare the data, then you make the data from dirty to clean, then you start analyzing the data."

"How do you remove duplicates from the country column? Data cleanup, remove duplicates."

"With the unique function, we can instantly clean our datasets, removing duplicates or extracting a list of distinct items for use in other Excel tools like data validation."

"Cleaning data refers to the process of tidying up data sets, making them consistent and presentable."

"The consequences if you don't clean your data prior to analysis is that you might end up with inaccurate results."

"Knowing how to clean data in Excel is actually extremely useful."

"So let's clean up our data a bit now. What do we need to clean? Straight away, this is not good for our data, this encoded value here."

"Data cleaning is like brushing your teeth. It's something you should do and do properly because otherwise, it can cause serious problems."

"All of these scenarios we need to know how to clean up that data and make it tidy."

"Cleaning data is just making sure that it's formatted correctly, it's consistent, and any errors or things that could cause us problems when we analyze it, we want to make sure that those have been removed."

"Here comes another challenge, and this is in line with the data cleaning strategies that we are trying to pick up."

"We want to do some cleaning before we actually get into analysis, and it's very important that we first drop any of the columns or rows that we don't want to keep before we continue on, or else we might waste time cleaning up columns that we won't end up using."

"Verification is a process to confirm that a data cleaning effort was well executed and the resulting data is accurate and reliable."

"99% of data science work is extracting and cleaning data, not really analyzing it."

"You can also look all the way back to when we did our original data cleaning."

"Steps of pre-processing of data: importing necessary libraries, reading the dataset, sanity check, exploratory data analysis, missing value treatments, outlier treatments, checking for duplicates and garbage value, normalization, encoding of categorical data."

"...it will keep what you can see but it's going to throw away these underlying formulas and then you can safely delete out that dirty column and you have your nice clean column."

"Data cleaning is a crucial step in the data science pipeline, ensuring that data is accurate, consistent, and ready for analysis."

"You just have to clean it up a little bit, brush it up a little bit, just to make it look exactly the way that you want it to look."

"You can use 'distinct' to eliminate duplicates."

"I actually really enjoy cleaning data. There's something that's a little bit of like numerical detective work when you're digging into your data."

"There's just something really satisfying about cleaning your data... knowing that my data are in order."

"Now it's time to talk about data cleaning, we have arrived to that point in our tutorial."

"Data scientists spend the majority of their time in data cleaning, and data cleaning is very essential because the data that comes in real life is very, very messy."

"Numpy and pandas allow you to do data cleaning and data exploration."

"Power Query is used to clean the data because our analysis is done only on the clean data."

"Cleaning data is essentially the idea of trying to correct or fill in any missing values or remove those bits completely."

"As they often say in data science, about 80-90% of your work is actually cleaning data."

"I want to strip that off of there so now I should have a nice clean data packet."

"It is really important and to do it we are going to use a library called Pandas."

"Data cleaning is fun; I don't mean to say it's not fun."

"It's really a matter of cleaning up your data and finding a way of representing it so that it tells the story that you're after."

"Cleaning data is the process that makes the data uniform without changing their meaning."

"The first part of building any analytical application is often dealing with some of the most mundane problems around data access, data loading, and basic data manipulation and cleaning."

"This data cleaning stuff... you get these real-world data sets, and it's like, they're not spelling it correctly, there's all sorts of crazy stuff."

"Regular expressions can be super useful in data cleaning."

"Data cleaning is important and that's a big part of what a data analyst does."

"Data cleaning is what 90% of it was going to be anyways."

"What we're going to work on in this video is how to take the words that are super common in this corpus and remove them."

"Now the next step is we're going to create a custom function to remove all the columns with nulls and rows with nulls."

"It's a really awesome way of quickly cleaning your records so that people can start consuming that data."

"The correct answer is C: Trim function which is used to remove unwanted spaces."

"Removing extraneous data helps make the remaining pattern more obvious, and that's going to help the learning process."

"This any_values could be useful in cleaning up your messy data and this allows you to do data munging or data wrangling."

"We're building a predictive model to clean missing data so that we can actually get a more accurate predictive model on all of it."

"Sometimes you have data and you need to remove extra spaces. Extra spaces are any spaces except for a single space between words."

"You need to remove seasonality, trending, and things like that."

"Power Query really is an amazing new tool that helps us clean the data and the beauty of it is everything is refreshable."

"You can ensure that this third-party software removes a lot of that information automatically for you."

"Transform your data and clean it up before it's imported."

"Data cleaning is extremely important; a lot of our models will not run if the data is not of the correct type or in the correct format."

"Removing missing values or NA values from an object is a very common operation in data analysis."

"Now the reason I'm doing this is so that my data, when I actually add it into a panda's data frame, I have a little bit cleaner of data."

"Data cleaning is a process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted."

"Once we've written this script, we can run clean and it will apply Bayes rule for us, happily cleaning our dataset."

"The real strength of Open Refine is to do batch transformations, especially for inconsistent spellings."

"In any machine learning problem, let's really focus on cleaning our dataset."