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In this comprehensive video by Simplilearn, we dive into the essential process of data cleaning in data mining. We start with a brief overview of data mining, exploring how it helps businesses uncover valuable insights from large datasets. Then, we move into the heart of the topic, explaining what data cleaning is and why it's a critical step in ensuring accurate and reliable data analysis. You'll learn about the steps involved in data cleaning, from removing duplicates and handling missing data to standardizing formats and correcting errors. Finally, we introduce the top 6 tools for data cleaning, showcasing how each tool can streamline the process and improve data quality for better decision-making. Whether you're a data enthusiast or a professional looking to sharpen your skills, this video will provide valuable insights into mastering data cleaning techniques for effective data mining.
00:00:00 - Introduction
00:01:02 - What is Data Mining?
00:01:33 - What is Data Cleaning?
00:01:56 - Why Data Cleaning is Important ?
00:03:14 - Data Import and Understanding
00:03:40 - Remove Duplicates
00:04:09 - Handle Missing Data
00:04:48 - Correct Structural Errors
00:05:16 - Standardization and Normalization
00:05:49 - Handling Outliers
00:06:13 - Filter Unnecessary Data
00:06:33 - Handle Inconsistent Data
00:06:54 - Data Validation and Integration Checks
00:07:24 - Export Cleaned Data
00:08:12 - OpenRefine
00:08:36 - Trifacta
00:08:49 - Tibco Clarity
00:09:04 - WinPure
00:09:24 - Melissa
00:09:37 - Data Ladder
00:10:00 - Quiz
Data cleaning in data mining is essential for improving data quality and ensuring accurate analysis. It involves identifying and fixing errors, inconsistencies, and inaccuracies in datasets. This includes handling missing values by replacing or removing them, eliminating duplicates, correcting typos or incorrect formats, and standardizing data. Outliers, which can distort results, are also addressed by either adjusting or removing them. The goal is to ensure the data is reliable and structured properly for analysis, leading to more accurate and actionable insights for decision-making in businesses. Effective data cleaning eliminates biases and enhances the overall integrity of the data used in analysis.

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➡️ About Post Graduate Program In Data Analytics
This Data Analytics Program is ideal for all working professionals and prior programming knowledge is not required. It covers topics like data analysis, data visualization, regression techniques, and supervised learning in-depth via our applied learning model with live sessions by leading practitioners and industry projects.
✅ Key Features
- Post Graduate Program certificate and Alumni Association membership
- Exclusive hackathons and Ask me Anything sessions by IBM
- 8X higher live interaction in live online classes by industry experts
- Capstone from 3 domains and 14+ Data Analytics Projects with Industry datasets from Google PlayStore, Lyft, World Bank etc.
- Master Classes delivered by Purdue faculty and IBM experts
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Resume preparation and LinkedIn profile building
- 1:1 mock interview
- Career accelerator webinars
✅ Skills Covered
- Data Analytics
- Statistical Analysis using Excel
- Data Analysis Python and R
- Data Visualization Tableau and Power BI
- Linear and logistic regression modules
- Clustering using kmeans
- Supervised Learning
? Learn More At: https://www.simplilearn.com/post-graduate-program-data-science?utm_campaign=CMT8NhAMgpg&utm_medium=Description&utm_source=youtube