Data Cleaning In Data Mining | Step by Step Data Cleaning Process | Data Cleaning | Simplilearn

Jan 15, 20250 likes87 views

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.

Data Cleaning In Data Mining | Step by Step Data Cleaning Process | Data Cleaning | Simplilearn

What is Data Cleaning ?
It is the process of identifying and correcting errors, inconsistencies, and
inaccuracies in a dataset to improve its quality and reliability.

Why Data Cleaning is Important?
Messy
Missing
values
Unreliable Data
Insights

Steps in Data Cleaning
Data Import and Understanding
01

Steps in Data Cleaning
Remove Duplicates
02

Steps in Data Cleaning
Handle Missing Data
03

Steps in Data Cleaning
Correct Structural Errors
04

Steps in Data Cleaning
Standardization and Normalization
05

Steps in Data Cleaning
Handling Outliers
06

Steps in Data Cleaning
Filter Unnecessary Data
07

Steps in Data Cleaning
Handle Inconsistent Data
08

Steps in Data Cleaning
Data Validation and Integration Checks
09

Steps in Data Cleaning
Export Cleaned Data
10

QUIZ
Which of the following is NOT a common Data Cleaning Technique ?
(A) Removing duplicate records
(B) Handling missing data (D) Adding random noise to data
(C) Normalizing data

The global data cleaning tools market is growing due to increased digitization from the COVID-19 pandemic. Data cleaning is the process of removing duplicate, inaccurate, or incomplete data from databases. It is important for obtaining clean data that can be analyzed without false conclusions. The benefits of data cleaning include removing errors, better reporting, and increased productivity from high-quality data.

Editing, cleaning and coding of data in Business research methodology VaishaghMp

Cleaning and editing of data involves reviewing collected data to remove errors and inconsistencies. This includes checking for missing answers, answers in the wrong place, or logically inconsistent responses. Coding converts qualitative data into quantitative data by assigning numerical values to response choices. It allows large amounts of information to be reduced to an easily handled form. Both cleaning and coding improve the quality and accuracy of collected data.

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Taking the following 6 steps can help improve data quality: 1. Perform a data audit to identify errors and gaps in current data. 2. Conduct a systems audit to ensure tools are integrated and functioning properly. 3. Revise data capture processes like forms and surveys to reduce errors. 4. Correct existing data errors by cleaning, deduplicating, and standardizing records. 5. Implement email alerts and reports to monitor data quality over time. 6. Manage data quality practices across departments to maintain high standards.

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Poor data quality can cost businesses millions in lost opportunities and inefficiencies. Automated Data Validation is the key to ensuring error-free, high-quality data at scale. In this insightful presentation, you'll learn: ✅ Why data validation is critical for business success ✅ The role of AI & cloud-based tools in data validation ✅ Best practices to implement error-free data workflows ✅ Use cases across finance, healthcare, retail, and more Stay ahead with data-driven decision-making! Download and explore now!

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This document provides guidance on using data analytics for internal auditing. It discusses key concepts like defining data analysis, getting data from various sources, choosing the right data analysis software, developing a data analysis methodology, and examples of data analytics techniques for analyzing journal entries and employee expenses. The document also covers best practices for critical success factors and infrastructure needed to support an effective data analytics program.

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In today’s data-driven world, the quality of your data determines the quality of your insights and business decisions. Data cleansing, also known as data scrubbing, is crucial for ensuring that your data is accurate, complete, and reliable. Here’s a quick guide to understanding the importance of data cleansing and its benefits: Steps Involved in Data Cleansing: Remove Irrelevant or Duplicate Content: Eliminate unwanted data to maintain dataset integrity. Resolve Structural Errors: Correct inconsistencies such as typographical errors and mislabeled classes. Manage Unwanted Outliers: Assess and remove erroneous outliers. Deal With Missing Data: Handle missing values effectively to maintain data integrity. Validate Data: Ensure data accuracy and reliability. Features of Quality Data: Validity: Conforms to business rules Accuracy: Matches true values Completeness: Free from errors and duplicates Consistency: Uniform across datasets Uniformity: Consistently measured across datasets Benefits of Data Cleansing Services: Improved Marketing Analysis: Target the right customers effectively. Time-Saving: Free up employee time for core tasks. Elimination of Inaccurate Data: Support sound business decisions. Enhanced Customer Satisfaction: Boost customer loyalty with accurate information. Increased Efficiency: Streamline business processes with accurate data. Enhance your business decisions with clean, accurate data. Contact us today to learn more about our professional data cleansing services

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1. Preprocessing occurs before any code is compiled and initializes values or evaluates anything. Code can be swapped or replaced during this stage but no computations occur yet. 2. Data preprocessing is an important initial step for data mining and machine learning projects. It involves cleaning data by removing out of range values, impossible combinations, and missing data to avoid misleading results from analyzing poor quality data. Preprocessing can take significant time but produces the final cleaned training data. 3. Data preprocessing methods include cleaning, selecting instances, normalizing, transforming, extracting features, and selecting features to standardize data formats and represent inputs while minimizing data or dimensionality.

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Data quality is the process of ensuring data values conform to business requirements. It is important for business intelligence projects which involve data integration from multiple sources. Pentaho Data Integration and DataCleaner are open source tools that can be used together for data integration and quality tasks like extraction, transformation, loading, cleansing and profiling. Performing data quality as part of the ETL process through tools like these helps standardize processes and improve scalability.

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The document discusses various aspects of data preparation including data issues, the data preparation process, reasons for data preparation, benefits of data preparation, and key steps in data preparation such as data profiling, cleaning, integration, transformation, discretization, and binning. Specifically, it covers profiling data to ensure quality, cleaning data by handling anomalies and missing values, integrating and enriching data from multiple sources, transforming data for modeling purposes, discretizing continuous variables, and binning data to reduce effects of small errors. The overall goal of data preparation is to organize and structure raw data for analysis and modeling.

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