Top 10 Techniques of Data Cleaning in Data Analytics

As it helps to ensure that the data utilised for analysis is correct, consistent, and dependable, data cleaning is an essential stage in the data analytics process. The top 10 methods for cleansing data in data analytics are listed below:

Table of Contents

Removing duplicate in data Analytics

Identify key columns

Find the column(s) or combination of columns in the dataset that uniquely identify each record. These important columns will be utilised to spot duplicates.

Sort the dataset

Based on the important columns found in the previous phase, sort the dataset. Similar records will be grouped together when the data is sorted, making it simpler to spot duplicates.

Compare adjacent records

Compare each record with the one adjacent to it as you iterate through the sorted dataset. The key columns should match to show a duplicate entry.

Decide on duplicate handling:

Once duplicates have been located, choose the best course of action. You have the option to maintain the most recent occurrence and delete older duplicates, or to keep the first occurrence and delete later ones. As an alternative, you can keep the information by giving each duplicate entry a special identification.

Remove duplicates

Delete the duplicate records from the dataset, keeping only the desired occurrence based on your chosen approach.

Validate the results

Check to see if any duplicate records remain in the dataset after duplication has been removed. To make sure the data is accurate, conduct a duplicate detection process once more or run a fast check.


It’s important to remember that duplicates might be found using criteria other than key columns, including a combination of columns or particular data patterns. The method described above offers an overview for eliminating duplicates, but you might need to modify it to meet the unique needs of your dataset and study.

Handling missing values in Data Analytics

In order to provide accurate and trustworthy data analysis, handling missing values is a crucial stage in the data cleaning process. Here are a few methods frequently used to deal with missing values:

Deleting rows or columns

You can decide to eliminate the rows or columns with missing values if there are not many missing data or if their absence has little influence on the analysis. However, use caution while employing this strategy since it might result in information loss and possible distortion in the data.

Using statistical techniques, update

Imputation involves replacing estimated values obtained through statistical methods for the missing ones. Mean imputation, median imputation, mode imputation, and regression imputation (using a regression model to estimate missing values based on other variables) are a few examples of common imputation techniques.

Imputation using machine learning algorithms

Based on the links between variables or patterns in the data, advanced approaches like multiple imputation or k-nearest neighbours (KNN) imputation can be used to fill in missing values.

Time-based imputation

You can use interpolation techniques like linear interpolation, spline interpolation, or time-based averages to impute missing values if your dataset contains time series data.

Creating an indicator variable

You can establish a second binary indicator variable that indicates if a value is missing or not in instead of directly impute missing data. This enables you to save the analysis’s missingness information.

Domain-specific imputation:

Expert input or domain knowledge may be used in some circumstances to impute missing data. For instance, if there are gaps in your client age data, you might be able to fill them in by consulting outside sources or making fair guesses.


It is crucial to assess the effects of various missing value handling strategies on the analysis’s findings and take into account each technique’s drawbacks and underlying presumptions. The kind of missing data, how they are distributed, and the unique requirements of the analysis all influence the approach selection.

Outlier detection and treatment in Data Analytics

In order to recognize and manage data points that significantly differ from the rest of the dataset, outlier identification and treatment is a vital step in data cleansing. The accuracy and dependability of the outcomes of data analysis might be negatively impacted by outliers. The steps in outlier discovery and treatment are as follows:

Define the criteria for outliers:

Establish the standards or cutoffs that, for your particular dataset, define what constitutes an outlier. Statistical measurements like z-scores, standard deviations, or percentiles, as well as subject-specific information, may be used to support this.

Visual exploration:

To visually spot probable outliers, plot the data using visualisations like scatter plots, box plots, or histograms. Look for data points that are distant from the central cluster or that show odd patterns.

Statistical methods:

Statistical approaches should be used to statistically detect outliers. The Tukey’s fences approach, which employs interquartile range (IQR) to find outliers, and the z-score method, which calculates the amount of standard deviations a data point deviates from the mean, are two often used techniques.

Domain expertise:

Apply your domain expertise to spot outliers based on contextual information. A few data points could be legitimate outliers because of certain conditions or events. To evaluate if the detected data are actual outliers or mistakes, consult subject-matter experts.

Decide on outlier handling:

Select a suitable treatment strategy for outliers when they have been located. If the outliers are thought to be inaccurate or irrelevant to the analysis, you might decide to eliminate them. The outlier numbers can also be transformed to bring them into a reasonable range or assigned as missing values for more research.

Sensitivity analysis:

Conduct a sensitivity analysis to determine the effects of various outlier management techniques on the analysis’s findings. To assess the robustness of the study, compare the results after outliers have been eliminated, altered, or kept.

Decide on outlier handling

Select a suitable treatment strategy for outliers when they have been located. If the outliers are thought to be inaccurate or irrelevant to the analysis, you might decide to eliminate them. The outlier numbers can also be transformed to bring them into a reasonable range or assigned as missing values for more research.

Sensitivity analysis:

Conduct a sensitivity analysis to determine the effects of various outlier management techniques on the analysis’s findings. To assess the robustness of the study, compare the results after outliers have been eliminated, altered, or kept.

Document and justify

substantiate and support: Include the reasoning behind the selected technique, any domain knowledge concerns, and the impact on the analysis in your documentation of the outlier identification and treatment process. This documentation will promote openness and

Standardizing and normalizing data in Data Analytics

Numerical data is transformed into a single scale using the procedures of standardisation and normalisation, which eliminate the impact of various units and ranges. In data analytics, these methods are especially helpful for comparing variables with various measurement scales and ensuring fair comparisons. An outline of normalisation and standardisation is provided below:


  • Calculate the mean (average) and standard deviation of the variable you want to standardize.
  • Subtract the mean from each data point.
  • Divide each resulting value by the standard deviation.
  • The standardized values will have a mean of 0 and a standard deviation of 1.

The formula for standardization is:

  • z = (x – μ) / σ


  • z is the standardized value.
  • x is the original data point.
  • μ is the mean of the variable.
  • σ is the standard deviation of the variable.

The data are changed by standardisation to have a normal distribution with a mean of 0 and a standard deviation of 1.


  • Determine the range or scale you want to normalize the data to (e.g., 0 to 1 or -1 to 1).
  • Calculate the minimum and maximum values of the variable.
  • Subtract the minimum value from each data point.
  • Divide each resulting value by the range (maximum value minus minimum value).
  • The normalized values will fall within the desired range.

The formula for normalization is:
x’ = (x – min) / (max – min)


  • x’ is the normalized value.
  • x is the original data point.
  • min is the minimum value of the variable.
  • max is the maximum value of the variable.

Normalization scales the data to a specific range, preserving the relative relationships between the data points.

When statistical approaches presume normality or when the distribution of the data is close to normal, standardisation is frequently applied. When you wish to put data into a consistent range, especially when the range of values varies widely among variables, normalisation might be helpful.

Correcting inconsistent values in data analytics

To maintain data quality and consistency, correcting incorrect values is a crucial stage in the data cleaning process. Misspellings, inconsistent formats, and values that go against established business limitations and standards are all examples of inconsistent values. Here are several methods for adjusting values with inconsistencies:

Standardizing formats:

Identify data fields with inconsistent formats, such as dates, addresses, or phone numbers, and apply formatting rules to ensure consistency. For example, converting all dates to a specific format or normalizing phone numbers to a standardized format.

Spell-checking and autocorrection:

Use libraries or algorithms for spell-checking to find and fix typos in textual data. Based on predefined dictionaries or machine learning models, autocorrection features can automatically substitute or propose repairs for misspelt words.

Using reference data:

Use reference data sources to check and rectify values, such as dictionaries, domain-specific terminology lists, or lookup tables. Replace or flag values that do not match by comparing the dataset’s values to the reference data.

Regular expressions and pattern matching

Use pattern matching or regular expressions to find mismatched values based on specified patterns or rules. For instance, pattern matching may be used to find and fix erroneous social security numbers or email addresses.

Business rule validation:

Verify the values against existing business restrictions or rules. For instance, make sure that numerical values are within anticipated ranges or that categorical variables include alternatives that are appropriate. Validate values or flag those that go against the rules.

Manual review and correction

In situations when automated methods fall short, manual evaluation and adjustment may be required. To find and fix values that are inconsistent and are based on domain knowledge or expert judgement, human involvement is required.

Data enrichment and external sources:

To enhance your dataset and check or fix incorrect values, use external data sources or APIs. For instance, validating and fixing addresses using geocoding services, or confirming data utilising third-party data sources.

Types of Data in Data Science:

Handling categorical data in Data Analytics

Considering that many machine learning algorithms and statistical approaches demand numerical inputs, handling categorical data is a crucial component of data cleaning and preparation. The following are some methods for dealing with categorical data:

One-Hot Encoding

One-hot encoding is a widely used method for representing binary vectors of categorical information. A binary variable (0 or 1) is used to represent each distinct category, adding new columns to the dataset. By using this method, category data can be processed and understood by algorithms. Be cautious when working with categorical variables that have a high cardiacity, though, as this might lead to a lot of extra features and possible problems like the curse of dimensionality.

Label Encoding:

Each category in the variable is given a special number label through label encoding. The corresponding integer value is substituted for each distinct category. When the categorical variable has an intrinsic ordinal relationship, which means the categories have a certain order or ranking, label encoding is helpful. Use label encoding with non-ordinal categorical variables, but use caution because it could inject unintentional ordinality into the data.

Frequency Encoding

The frequency (count) of occurrences in the dataset is substituted for each category by frequency encoding. When the frequency of a category is instructive and pertinent to the analysis, this method may be helpful.

Ordinal Encoding:

When categorical variables have an ordered relationship but the distances between categories are meaningless, ordinal encoding is appropriate. Depending on the intended order, it gives each category a numerical value.

Hashing Trick

The hashing strategy reduces the dimensionality of data by transforming categorical variables into fixed-length numerical representations. By converting categories to numbers using hash functions, it is possible to decrease the number of distinct categories while retaining some amount of information.

Manual Encoding:

Depending on the circumstances and your subject expertise, you can decide to manually encode categorical variables. Using this method, you can give categories unique number values based on their importance or desired representation.

The type of data, the relationships between the categories, the needs of the study, and the algorithms or models being employed all influence the categorical encoding technique that is selected. It is crucial to take into account how encoding affects the interpretation and effectiveness of the analysis, as well as any potential drawbacks or presumptions related to each technique.



Parsing and transforming data

In order to assure consistency and compatibility across the dataset, parsing and transforming data entails dividing data fields into several columns, extracting pertinent information, and altering data formats. Here are several methods for data transformation and parsing:

Splitting text fields:
Text fields can be divided into separate columns using delimiters or patterns if they include many pieces of information, such as full names or addresses. Creating separate columns for the first and last names of a full name, for instance.

Substring extraction:
Using pattern matching or string manipulation tools, extract particular substrings from text fields. For instance, retrieving the year from a date field or the domain from an email address.

Conversion of data formats:
Data blending: 

Creating new composite columns by combining data from other columns or fields. Concatenating text fields, combining values from related columns, or averaging information across numerous rows can all be used to do this.

Cleaning and transforming textual data:

To standardise textual data for analysis, use text cleaning procedures such eliminating special characters, changing text’s case to lowercase, removing stop words, or conducting stemming/lemmatization.

Scaling and normalization: 

To achieve fair comparisons, either scale numerical data to a common range or normalise data. As mentioned in a prior response, this aids in reducing the impact of various units and ranges among variables.
Convert data formats to maintain compatibility and consistency. This covers converting text information to standardised forms for date and time, numbers, or both. For instance, changing the date format from “MM/DD/YYYY” to “YYYY-MM-DD”.

Feature engineering:

Using mathematical transformations, domain-specific calculations, or interaction terms, produce new features or variables based on current data. To capture extra data for analysis, may entail constructing ratios, percentages, aggregations, or other derived variables.

Handling nested or hierarchical data:

You might need to parse and transform nested or hierarchical data, like JSON or XML, into a tabular format that is appropriate for analysis. This may entail breaking up nested structures into different columns, removing particular elements, or flattening nested structures.

Maintaining data integrity, taking into account data quality concerns, and documenting the actions used to assure reproducibility are crucial when parsing and manipulating data. The particular methods used will be determined by the data format, the desired result, and the analysis.


Dealing with data integration issues in data analytics

Data integration is the process of bringing together information from several sources or systems into a single, uniform format. There may be a number of problems that need to be resolved during the data integration process. Here are some typical problems with data integration and solutions for them.

Data inconsistencies: 

When the same entity or attribute is represented differently by various sources, there is a data inconsistency. You can address this by performing data analysis and profiling to find irregularities and creating rules or transformations to standardise the data. For instance, you might have to make sense of disparities in data formats, measurement units, or naming conventions.

Data duplication:

The presence of duplicate or repeated data across many sources is referred to as data duplication. Techniques like record linkage, which identifies and merges duplicate records based on shared identifiers or similarity metrics, can be used to address it. Algorithms and strategies for deduplication can be used to find and get rid of duplicate data entries.

Schema integration

Schema integration is the process of balancing variances in the format and organisation of data schemas from various sources. The attributes and relationships in various schemas are mapped to generate a single, integrated schema. This can be done using schema mapping and transformation techniques. To conform to a standard schema, this may entail renaming, rearranging, or aggregating attributes.

concerns with data quality: 

Data integration may reveal concerns with data quality, including missing numbers, erroneous data types, or outliers. These problems can be solved and trustworthy and consistent data across sources can be ensured by using the data cleansing procedures that were previously outlined. Data purging, standardisation, outlier detection, and imputation techniques might be used in this.

Data integration frequently involves handling enormous amounts of data, which can provide storage, processing, and performance difficulties. To effectively handle large-scale data integration, methods including data segmentation, parallel processing, and data compression can be used. Utilising cloud-based services or distributed computing frameworks can also aid in managing and processing enormous amounts of data.

Data integration includes merging data from several sources, each of which may have its own security and privacy needs. In order to protect sensitive data, it is imperative to address data security and privacy concerns during integration, ensuring compliance with applicable laws and using encryption, access controls, or anonymization techniques.



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Handling data inconsistencies across datasets

Finding and resolving variances in values, formats, and structures is necessary for handling data inconsistencies between datasets. To guarantee data compatibility and integrity, steps include data profiling, standardisation, cleaning, mapping, deduplication, documenting, and validation. Understanding the data, business context, and integration requirements call for a systematic approach.

Data consistency management across datasets:

  1. Data profiling: Examine data to find discrepancies.
  2. Standardise data by creating guidelines for formats and values.
  3. Address missing values, erroneous kinds, and outliers while cleaning up data.
  4. Align characteristics and change data structures through data mapping.
  5. Merge duplicate records or entities using record linkage and deduplication.
  6. Record decisions and modifications for documentation and data lineage.
  7. Verify precision and consistency through data validation and quality assurance.
  8. These procedures guarantee data compatibility and integrity across datasets.

Validating data accuracy in Data Analytics

Validating data accuracy is a crucial stage in data preparation and cleaning to guarantee the quality and dependability of the data. Here are some essential criteria to check the veracity of data:

Data profiling

Statistical analysis and summary calculations are used in data profiling to find potential problems with the quality of the data. This entails verifying data distributions, reviewing summary statistics, and spotting outliers or extreme results.

Cross-field validation

Cross-field validation involves running tests that require comparing data from various fields or variables to look for contradictions or anomalies. If you have a dataset with fields for age and birth date, for instance, you can check to see if the age matches the age that was determined using the birth date.

Referential integrity checks

Validate connections and dependencies between relevant datasets or tables using referential integrity checks. This entails examining the validity and consistency of data references as well as the maintenance of foreign key links. For instance, making sure the customer master table contains the customer ID mentioned in a sales transaction table.

Business rule validation

Validate data in accordance with established corporate standards or restrictions. This entails determining whether data values conform to prescribed patterns, fall within predicted ranges, or satisfy particular criteria depending on business needs. For instance, verifying that email addresses have the proper format or if sales revenue amounts are positive.

Duplicate detection

Find and remove duplicate records or entries from the dataset using duplicate detection. Comparing fields or groups of fields to spot probable duplicates and putting into practise

External data validation:

Utilise references or external data sources to validate or cross-verify the veracity of the data. This is known as external data validation. This may entail cross-referencing data with reliable sources, performing data enrichment utilising reliable data, or using third-party APIs or services to validate information.

Data sampling and manual review

Data sampling and manual review: To confirm the accuracy, take representative samples of the data and carry out manual inspections or reviews. This entails carefully checking a sample of the data to ensure its accuracy, contrasting it with reliable sources, or getting advice from subject-matter experts.

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