Data Cleaning

Data Cleaning is a crucial process in data management that involves identifying and correcting inaccuracies or inconsistencies in datasets. This tag encompasses various workflows designed to enhance data quality, including data validation, deduplication, and normalization techniques. By exploring these workflows, you can improve your data analysis and ensure reliable insights. Dive into our curated list of Data Cleaning workflows to streamline your data preparation process and boost your project's success!

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🔍Dataset Cleaning🔍

🧹✨Step 1: Clean the Dataset and prepare for analysis🧹✨

Data ProcessingData Cleaning+2
Masnoon Ahmed
871 runs
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Filter and Split

Filter or split a spreadsheet into component parts.

Data ManipulationData Processing+1
J
Julius
445 runs
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Creating correlation matrix on genetic dataset

This workflow guides you through creating a correlation matrix using real-world data. We will analyze 11 genes linked to breast cancer tumor development to observe expression rate variations between normal breast tissue and breast cancer tissue. The dataset comprises 572 entries, including gene names, sample information, and FPKM values (Fragments per kilobase per million mapped fragments). Video Guide: https://youtu.be/G1yKwhbLTQU?si=i7zDg4isGkXhTRGw Community Guide: https://community.julius.ai/t/guide-creating-a-correlation-matrix-on-gene-expression/4238

GeneticsData Visualization+3
Alysha.G
283 runs
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Analyze and Deduplicate Excel Files

No description available

Data CleaningData Preparation+2
Alysha.G
122 runs