Data Cleaning
20 Threads
5 super dirty datasets for you to practice your data cleaning on using either Excel, Power Query, SQL, Python or R. Kindly like and retweet.
Data cleaning is one of the most important skills for a Data Analyst. Not Excel. Not SQL. Not PowerBI Without clean data, any analysis done is unreliable. Here's my Data Cleanin...
The 10 most common errors you make when analyzing #SEO data. These are quite dangerous for your websites. BUT they can be easily fixed if you know them. Let's see the full list...
No model has ever beaten 91% accuracy on ImageNet! Because this dataset has over 100,000 mislabeled images. Folks at MIT have released a tool that automatically detects such issu...
Most people don't know this: MNIST is the most popular dataset in Machine Learning, but despite millions of people trying, there has never been a model that solves it with 100% ac...
When preparing data for analysis remember these steps: 1. Identify missing values 2. Handle missing values 3. Check for inconsistencies in the data 4. Standardize the data 5. Tran...
Day 38 of #60daysOfMachineLearning π· Pandas - Removing Duplicates π· Finding and removing duplicates is important because it can help prevent errors and biases, save space and mem...
Day 37 of #60daysOfMachineLearning π· Pandas - Fixing Wrong Data π· "Wrong data" does not have to be "empty cells" or "wrong format", it can just be wrong. If you look at the dat...
The 10 most common errors you make when analyzing #SEO data. These are quite dangerous for your websites. BUT they can be easily fixed if you know them. Let's see the full list...
Day 35 of #60daysOfMachineLearning π· Pandas - Cleaning Empty Cells π· π¦ Remove Rows Empty cells can potentially give you a wrong result when you analyze data. One way to deal wit...
Data Analyst Skill Set Checklist β Math and Statistics β Excel β SQL β Data Visualization (Tableau/ Power BI) β Data Cleaning β Scripting Language ( Python / R ) β Data Storytelli...
π¨ Free data cleaning cheat sheet! π¨ Data cleaning takes up 80% of the data science workflow. π± So, we created a checklist to help you identify and resolve any quality issues with...
I get tagged to a lot of visualizations lately and I wanted to say youβre all doing amazingly well. However, I want to point out somethings that might help in your next dashboard...
How do you "clean" data? When I first got into the data world this was an extremely confusing concept for me to understand, but incredibly important to the data process. A thread...
Data Scientists spend 80% of their time in Data Cleaning 5 tutorials that will make you better at data cleaning:
If machine learning is your bread and butter, these 5 resources will act as a saviour. (A thread) π
Everyone knows they need to replace missing values in their dataset. Most people, however, miss one critical step. Here is what you aren't doing and how you can fix it: 1 of 9
One of the common challenges of Data Analysts is missing values in the datasets. In this thread, we will see how to deal with missing values in real-world datasets. A thread π§΅π h...
A Typical Machine Learning Workflow 1. Problem formulation 2. Data collection 3. Data analysis 4. Data cleaning 5. Selecting and training a model 6. Evaluating a model 7. Improvin...
The 4 stages of a machine learning project lifecycle: 1. Project scoping 2. Data definition and preparation 3. Model training and error analysis 4. Deployment, monitoring, and mai...