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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

What is Data Scrubbing?

Ayushi Trivedi
Ayushi Trivedi Last Updated : 12 Aug, 2024
7 min read

Introduction

Think of the fact that you’re planning a massive family gathering. You have a list of attendees, but it is full of wrong contacts, the same contacts and some of the names in the list are spelled wrongly. If you do not take your time to clean up this list, then there is every possibility that your reunion will be something of a disaster. As much as it goes for a companies and corporations require clean and accurate data in order to function properly and make right choices. The operation to clean your data, making sure that it is accurate, free of duplicates and is as recent as possible is referred to as data scrubbing. Data scrubbing, therefore, improves the operational performance and the decision makings of companies just like proper preparation does for the reunion.

What is Data Scrubbing?

Overview

  • Defining data scrubbing and learning why it is crucial.
  • To learn about data scrubbing some of the techniques and tools that can be used.
  • Understand some of the areas that most affect data quality and what can be done to correct the problems.
  • Learn more about ways by which data scrubbing can be effectively be implemented in your organization.
  • Identify the problems of data scrubbing and how to avoid them.

Table of contents

What is Data Scrubbing?

Data scrubbing is a data management process of pinpointing and fixing data entry problems such as accuracy issue and inconsistency in the data. Such problems can stem from errors such as wrong entries in data input, problems that occur in the computer databases as well as merging of data from various sources. This is important since analysis, reporting, and decision-making require feeding clean data into the process.

Steps Involved in Data Scrubbing

Data scrubbing pertains to the process of washing in that it entails a set of protocols to be followed to address and rectify issues with data. It usually involves checking, editing and normalizing the data in a bid to achieve accuracy and uniformity of data.

Data Validation

This step involves checking the data for errors and inconsistencies. It includes verifying that the data falls within acceptable ranges and adheres to predefined formats. For example, ensuring that dates are in the correct format (e.g., YYYY-MM-DD) and numerical values fall within specified ranges.

Duplicate Detection and Removal

This often results in having two or more entries with similar or identical information because of various causes including data entry mistakes, and problems that are associated with system interfaces. Data scrubbing also entails the process of weeding them out with a view of making sure that all the records in the dataset are not but a duplicate of one another.

Data Standardization

Different data sources may use varying formats or units. Data scrubbing includes converting data into a standardized format to ensure consistency across the dataset. For instance, standardizing date formats or converting all currency values to a common currency.

Data Correction

The input errors should be corrected; these comprise of typo-graphical errors, wrong entries on the input, and old information. Data rectification means correcting these mistakes in a bid to maintain the credibility and reliability of the dataset in question.

Data Enrichment

Sometimes, data scrubbing also involves adding missing information or enhancing existing data. This can include filling in missing values from external sources or updating records with the latest information.

Data Transformation

Transforming data into a format suitable for analysis or reporting is another aspect of data scrubbing. This can include aggregating data, creating new calculated fields, or restructuring data to fit analytical models.

Data Integration

When data comes from multiple sources, integrate it into a unified format. Data scrubbing ensures accurate and meaningful combination of data from different sources.

Data Auditing

Regular audits are performed to review the quality of data and the effectiveness of the data scrubbing processes. This helps in maintaining ongoing data quality and identifying areas for improvement.

Techniques and Tools for Data Scrubbing

Let us now look into the techniques and tools for data scrubbing below:

Techniques

  • Data Validation: Checking data against predefined rules or standards to ensure accuracy.
  • Data Parsing: Breaking down data into smaller, manageable pieces to identify errors.
  • Data Standardization: Converting data into a common format for consistency.
  • Duplicate Removal: Identifying and eliminating duplicate records in the dataset.
  • Error Correction: Manually or automatically correcting identified errors in the data.
  • Data Enrichment: Adding missing information or enhancing data with additional relevant details.

Tools

  • OpenRefine: An important means of cleaning and moving the data.
  • Trifacta: A data manipulation environment where a user is able to manage and prepare data with the help of artificial intelligence.
  • Talend: An electronic data warehouse that incorporates methods for effective data cleaning.
  • Data Ladder: A verosity driven tool, collecting and matching records of data.
  • Pandas (Python Library): Dirty data has been a thorn in the side of data analysts for years and data frame is a very flexible tool used in the handling of data and cleaning it up in the process.

Importance of Data Scrubbing

Data Scrubbing is an important process of ensuring that data is consistent and usable in a number of fields. Here’s why data scrubbing is essential:

Enhanced Decision-Making

Consequently, clean data is necessary, so that appropriate choices can be made in the right way. Misinformation can be very damaging since it can cause negative consequences to decision making of any strategic development or operational activities. That way organizations can be assured of quality data that can help in improving business performance.

Increased Efficiency

Thus, data scrubbing eliminates duplicate records and redundancies in the data, correct errors and standardize formats of the data which makes it easier to process data. This enhances the flow of work, reduces the time spent correcting incorrectly keyed data, and boosts productivity.

Improved Customer Relations

Well maintained customer databases improve the way businesses interact and address their clientele. This way, because of the reduction of errors and differences in the customers’ information, businesses are able to minimize their mistakes and give their customers the maximum satisfaction and loyalty which will eventually lead to increased clientele base.

Regulatory Compliance

This is partly because, numerous industries have legal obligations in terms of data accuracy and data privacy. Data scrubbing assists to complies with these regulations and therefore cut out possible legal cases as well as fines.

Cost Savings

It also means that with incorrect data a great many of money, time and other resources will be used in vain, as well as important opportunities will be missed. Organizations can avoid such costs since cleaning data means that there will not be frequent need for cleaning, corrections, and retrievals that may be very costly.

Enhanced Data Integration

Several different sources of data are used in organizations. Data scrubbing helps in getting data from different systems in a more comprehensive approach hence facilitating an integrated way of looking at the information most important for the analysis and reporting needs.

Better Analytics and Reporting

Analytics is a vital function in companies and organizations, but its effectiveness depends on the caliber of the data that is fed into it. With a good and clean data layer, data scrubbing helps to ensure that the data used for reports and analysis is constantly clean, resulting in reports and analysis that are as accurate as possible.

Common Data Quality Issues and Solutions

  • Missing Values: Use techniques like imputation, where missing values are replaced with estimated values, or remove records with missing data.
  • Inconsistent Data Formats: Standardize formats (e.g., dates, addresses) to ensure consistency.
  • Duplicate Records: Implement algorithms to identify and merge or remove duplicates.
  • Outliers: Detect and investigate outliers to determine if they are errors or valid values.
  • Incorrect Data: Validate data against trusted sources or use automated correction algorithms.

Best Practices for Data Scrubbing

  • Establish Data Quality Standards: It is also necessary to state what kind of data can be considered clean for an organization.
  • Automate Where Possible: Apply data cleaning automation and use scripts where it is impossible to employ data cleaning tools.
  • Regularly Review and Update Data: data scrubbing should indeed be an iterative process, it means that it should not be considered as a one-time shot.
  • Involve Data Owners: Discuss the matters with those people who know the data well, in order to detect and resolve problems.
  • Document Your Process: Keep detailed records of data cleaning activities and decisions.

Challenges in Data Scrubbing

  • Volume of Data: Working with Big data poses a challenge in how one deals and manages with big amount of data on hand.
  • Complexity of Data: The large proportions of data also diversify in nature, including structured, unstructured, text, numerical, categorical, nominal, ordinal, and more.
  • Lack of Standardization: Inconsistent data standards across sources complicate the cleaning process.
  • Resource Intensive: Data scrubbing can require significant human and technical resources.
  • Continuous Process: Maintaining data quality requires ongoing effort and vigilance.

Conclusion

A crucial step in guaranteeing the accuracy and dependability of data utilized in analysis and decision-making is data cleansing. Organizations may dramatically increase the quality of their data, resulting in more accurate insights and superior business outcomes, by putting best practices and efficient data cleansing processes into practice. Data scrubbing is an investment worth doing, despite the difficulties, because clean data has many advantages.

Frequently Asked Questions

Q1. What is data scrubbing?

A. Data scrubbing, or data cleansing, is the process of detecting and correcting errors, inconsistencies, and inaccuracies in datasets to improve data quality.

Q2. Why is data scrubbing important?

A. Data scrubbing ensures that data is accurate, consistent, and reliable, which is crucial for accurate analysis, reporting, and decision-making.

Q3. What are some common data quality issues?

A. Common issues include missing values, inconsistent data formats, duplicate records, outliers, and incorrect data.

Q4. What tools can be used for data scrubbing?

A. Tools like OpenRefine, Trifacta, Talend, Data Ladder, and the Pandas library in Python are commonly used for data scrubbing.

Q5. What are the challenges in data scrubbing?

A. Challenges include handling large volumes of data, dealing with complex data structures, lack of standardization, resource intensity, and the need for continuous effort.

data cleaningdata scrubbingData Validationnormalizing
Ayushi Trivedi
Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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Data Cleaning Using Pandas in Python
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