11 Essential Data Transformation Methods in Data Mining (2025)

Did You Know? Generative models, such as Large Language Models (LLMs), are being explored for their ability to generate synthetic data, fill missing values, and enhance data augmentation processes. This integration is opening new avenues for data transformation, especially in scenarios with limited data availability.

Data transformation is a critical process in data mining that involves converting raw data into a structured, usable format for analysis. The goal is to enhance data quality, reduce complexity, and improve the accuracy of the models used for decision-making. In 2025, the methods for data transformation continue to advance, with techniques that streamline the preparation of data for advanced analytics and machine learning.

In this blog, you’ll explore 11 essential data transformation methods, from data normalization to feature engineering, highlighting their impact on data quality, model performance, and the latest trends in data science.

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Data transformation in data mining converts raw, unstructured, or inconsistent data into a format suitable for analysis and mining. It ensures data is uniform, accurate, and ready for advanced algorithms. 

Key concepts include:

• Data Cleaning: Fixing inaccuracies using data cleaning techniques.
• Data Integration: Merging information from various sources.
• Data Normalization: Scaling data to uniform ranges, reducing biases.
• Data Discretization: Converting continuous data into categories.
• Feature Engineering: Creating or modifying features for better predictions.
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• While data transformation in data mining overlaps with other data preprocessing steps, its primary focus is on reshaping data to meet the structural or analytical needs of data mining models.
• Also Read: Data Preprocessing in Machine Learning: 7 Easy Steps To Follow

The Role of Data Transformation in Data Mining

Data transformation in data mining plays a crucial role in enhancing data quality, enabling efficient algorithms, and uncovering meaningful patterns for actionable insights. 

It is indispensable for achieving the following objectives:

• Improves Quality: Fixes inconsistencies and standardizes formats.
• Enhances Algorithms: Provides structured, scalable inputs.
• Enables Patterns: Enriches data to reveal trends.

Example: Retail sales data with inconsistent formats can be transformed to identify customer trends and optimize decisions.

Data transformation in data mining is the cornerstone of bridging the gap between raw inputs and actionable intelligence.

Also Read: 6 Methods of Data Transformation in Data Mining

To successfully transform data, it’s important to understand the techniques that can be applied. Let's take a closer look at the essential data transformation techniques.

What Are the Essential Data Transformation Techniques?

Essential data transformation techniques are methods that help you prepare raw data for analysis by converting it into a more suitable format. These techniques include normalization, standardization, aggregation, and dimensionality reduction, each playing a key role in improving data quality and model performance. 

Below, we’ll dive deeper into these methods and explain how they enhance your data mining and machine learning tasks.

Normalization

Adjusts numerical data to a standard range (e.g., [0,1]) to ensure consistency across features.

When to Use: Crucial in machine learning tasks like k-means clustering or deep learning, where differing scales can skew results.

How to Implement: Use formulas like Min-Max scaling or Z-score normalization. 

Pros: Removes scale dominance, ensures fair feature contribution.

Cons: Can amplify noise in small datasets.

Use Case: Normalizing variables like height and weight in health studies to compare their influence on outcomes equally.

Sample Code: 

from sklearn.preprocessing import MinMaxScaler
import numpy as np

# Sample data
data = np.array([[10], [15], [25], [50], [100]])

# Normalize data to [0, 1]
scaler = MinMaxScaler()
normalized_data = scaler.fit_transform(data)

print("Normalized Data:\n", normalized_data)

Output:

Normalized Data:
[[0.        ]
[0.05555556]
[0.16666667]
[0.44444444]
[1.        ]]

Explanation: This code normalizes the sample data to a range of [0,1][0, 1][0,1] using the MinMaxScaler. It’s helpful for features with different ranges to avoid skewing models like k-means or neural networks.

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Also Read: Normalization in SQL: 1NF, 2NF, 3NF & BCNF

Scaling

Converts data into a consistent scale or distribution, often standardizing to a mean of 0 and a standard deviation of 1.

When to Use: Essential for algorithms sensitive to variance, such as support vector machines or principal component analysis.

How to Implement: Use the standardization formula.

Pros: Ensures uniform input to algorithms.

Cons: Doesn’t address outliers effectively.

Use Case: Preparing sensor data for industrial automation systems, where measurements vary across scales.

Sample Code: 

from sklearn.preprocessing import StandardScaler

# Sample data
data = [[10, 20], [15, 30], [25, 50], [50, 100]]

# Scale data to mean = 0, std = 1
scaler = StandardScaler()
scaled_data = scaler.fit_transform(data)

print("Scaled Data:\n", scaled_data)
</> Copy Code

Output:

Scaled Data:
[[-0.97332853 -0.97332853]
[-0.64888568 -0.64888568]
[ 0.          0.        ]
[ 1.62221421  1.62221421]]

Explanation: Here, data is standardized so that it has a mean of 0 and a standard deviation of 1. Scaling ensures models like SVMs or PCA work efficiently with consistent feature magnitudes.

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Also Read: Data Preprocessing in Machine Learning: 7 Easy Steps To Follow

Data Aggregation

Combines data points into summaries, such as totals, averages, or medians, simplifying complex datasets.

When to Use: Common in time-series analysis, dashboards, or exploratory data analysis (EDA).

How to Implement: Group data by categories and calculate metrics. For example, summarizing daily web traffic into monthly averages.

Pros: Reduces computational load, aids in trend visualization.

Cons: Risks oversimplifying valuable details.

Use Case: Aggregating sales data by region to analyze geographic performance patterns.

Sample Code: 

import pandas as pd

# Sample data
data = {
    'Date': ['2025-01-01', '2025-01-02', '2025-01-01', '2025-01-02'],
    'Sales': [200, 300, 150, 350],
    'Region': ['North', 'North', 'South', 'South']
}
df = pd.DataFrame(data)

# Aggregate data by date and region
aggregated_data = df.groupby(['Date', 'Region']).sum().reset_index()

print("Aggregated Data:\n", aggregated_data)
</> Copy Code

Output:

        Date Region  Sales
0  2025-01-01  North    200
1  2025-01-01  South    150
2  2025-01-02  North    300
3  2025-01-02  South    350

Explanation: This code aggregates sales data by date and region using pandas. It simplifies complex data, making trends easier to analyze, such as regional sales performance.

You can get a better understanding of Python libraries with upGrad’s Learn Python Libraries: NumPy, Matplotlib & Pandas. Learn how to manipulate data using NumPy, visualize insights with Matplotlib, and analyze datasets with Pandas.

Also Read: Aggregation in MongoDB: Pipeline & Syntax

Discretization

Splits continuous data into discrete bins or intervals, often converting numeric data to categorical.

When to Use: Useful in decision trees, or when simplifying complex datasets for interpretability.

How to Implement: Data transformation techniques include equal-width binning (splitting into ranges) or quantile-based binning. For example, breaking ages into "18-25," "26-40," etc.

Pros: Simplifies relationships, reduces model complexity.

Cons: Can cause information loss.

Use Case: Categorizing temperatures into "Cold," "Moderate," and "Hot" for a weather prediction model.

Sample Code: 

from sklearn.preprocessing import KBinsDiscretizer
import numpy as np

# Sample data
data = np.array([[18], [25], [35], [45], [60]])

# Discretize data into 3 bins
discretizer = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy='uniform')
discretized_data = discretizer.fit_transform(data)

print("Discretized Data:\n", discretized_data)
</> Copy Code

Output:

Discretized Data:
[[0.]
[0.]
[1.]
[1.]
[2.]]

Explanation: This code bins continuous data (e.g., ages) into three categories using KBinsDiscretizer. Discretization is useful for simplifying numeric data into meaningful groups.

Also Read: Data Preprocessing In Data Mining: Steps, Missing Value Imputation, Data Standardization

Log Transformation

Compresses data ranges using the logarithm function, reducing the impact of extreme values.

When to Use: Effective for data with exponential growth trends or significant skewness.

How to Implement: Replace each value X with log(X). Ensure values are positive.

Pros: Reduces variance and highlights smaller patterns.

Cons: Not suitable for zero or negative values.

Use Case: Transforming financial data, like stock prices, which often exhibit exponential growth.

Sample Code: 

import numpy as np

# Sample data
data = np.array([10, 100, 1000, 10000])

# Apply log transformation
log_transformed_data = np.log(data)

print("Log Transformed Data:\n", log_transformed_data)
</> Copy Code

Output:

Log Transformed Data:
[2.30258509 4.60517019 6.90775528 9.21034037]

Explanation: The log transformation reduces data range and skewness, which is beneficial for datasets with exponential growth, such as income or population data.

Also Read: Data Analytics Lifecycle : A Comprehensive Guide Every Data Analyst Should Know

Reciprocal Transformation

Applies the reciprocal (1/x) of a numeric variable, reducing the impact of large values and transforming skewed distributions.

When to Use: When large values dominate the dataset or when dealing with inverse relationships.

How to Implement: Apply x′=1/x to all non-zero values in the dataset.

Pros: Reduces skewness, stabilizes variance, and diminishes the influence of outliers.

Cons: Cannot handle zero or negative values directly, which may require preprocessing.

Use Case: Transforming "travel time" data to emphasize shorter times over longer durations.

Sample Code:

import numpy as np

# Sample data
data = np.array([2, 4, 8, 16])

# Apply reciprocal transformation
reciprocal_data = 1 / data

print("Reciprocal Transformed Data:\n", reciprocal_data)
</> Copy Code

Output:

Reciprocal Transformed Data:
[0.5    0.25   0.125  0.0625]

Explanation: This code applies a reciprocal transformation to sample data, inverting values to reduce the range and impact of larger numbers. This technique is especially useful in datasets where smaller values are more meaningful or influential.

Also Read: Understanding Types of Data: Why is Data Important, its 4 Types, Job Prospects, and More

Square Transformation

Applies the square of a numeric variable, amplifying differences between larger values and enhancing their importance.

When to Use: When larger values are more significant in the analysis or when emphasizing variance is desired.

How to Implement: Apply x′=x2 to all values in the dataset.

Pros: Highlights the impact of larger values, improving model sensitivity.

Cons: May exaggerate outliers and increase data skewness.

Use Case: Squaring distances in physics to calculate energy-related metrics like potential energy.

Sample Code:

import numpy as np

# Sample data
data = np.array([2, 3, 4, 5])

# Apply square transformation
squared_data = np.square(data)

print("Squared Transformed Data:\n", squared_data)
</> Copy Code

Output:

Squared Transformed Data:
[ 4  9 16 25]

Explanation: This example squares the values in a dataset, magnifying the differences between larger numbers. Square transformation is especially effective when larger magnitudes carry more weight or significance in the analysis.

Also Read: Chi Square Test: Introduction, How to calculate, When to use, Properties

Square Root Transformation

Applies the square root of a numeric variable, reducing the impact of larger values while preserving relative differences.

When to Use: When handling skewed data or when reducing the scale of large values is necessary.

How to Implement: Apply x′=x​ to all non-negative values in the dataset.

Pros: Compresses large values while maintaining their order, stabilizes variance, and reduces skewness.

Cons: Cannot handle negative values directly and may require preprocessing.

Use Case: Transforming population data to reduce the effect of outliers in regression models.

Sample Code:

import numpy as np

# Sample data
data = np.array([4, 16, 25, 36])

# Apply square root transformation
sqrt_data = np.sqrt(data)

print("Square Root Transformed Data:\n", sqrt_data)
</> Copy Code

Output:

Square Root Transformed Data:
[2. 4. 5. 6.]

Explanation: This code applies the square root transformation to reduce the range of large values in the dataset. Square root transformation is particularly useful when analyzing data with a wide range of magnitudes, such as income or population metrics.

Also Read: How to Compute Square Roots in Python

Box-Cox Transformation

This is one of the data transformation techniques that transforms positive numeric data to stabilize variance and make it more normally distributed using a power transformation.

When to Use: When dealing with skewed data requiring normalization for statistical modeling or machine learning algorithms.

How to Implement: Use the Box-Cox formula.

where is a parameter to determine the type of transformation.

Pros: Handles skewed data effectively, adapts to various data distributions using .

Cons: Requires positive values; preprocessing may be needed for datasets with zeros or negatives.

Use Case: Transforming sales or revenue data with high skewness for regression models.

Sample Code:

from scipy.stats import boxcox
import numpy as np

# Sample data (must be positive)
data = np.array([1, 10, 100, 1000])

# Apply Box-Cox transformation
boxcox_data, lambda_value = boxcox(data)

print("Box-Cox Transformed Data:\n", boxcox_data)
print("Optimal Lambda Value:\n", lambda_value)
</> Copy Code

Output:

Box-Cox Transformed Data:
[0.         2.30258107 4.6051541  6.90771908]
Optimal Lambda Value:
 -1.517034554077419e-06

Explanation: This example uses the Box-Cox transformation to normalize highly skewed data, ensuring compatibility with algorithms that assume normally distributed inputs. The optimal  value is computed automatically to determine the best transformation.

Also Read: Unleashing the Power of Data Analytics

Attribute Construction

Generates new features by combining or transforming existing ones, adding meaningful context to datasets.

When to Use: When raw features lack predictive power or need domain-specific interpretation.

How to Implement: Use formulas or domain knowledge (e.g., speed = distance/time).

Pros: Enhances feature richness, improving model performance.

Cons: Computationally intensive, potential for overfitting.

Use Case: Creating a "profit margin" feature in business analytics by dividing profit by revenue.

Sample Code: 

import pandas as pd

# Sample data
data = {
    'Distance': [100, 200, 300],
    'Time': [2, 4, 6]
}
df = pd.DataFrame(data)

# Create a new attribute: Speed
df['Speed'] = df['Distance'] / df['Time']

print("Data with Speed Attribute:\n", df)
</> Copy Code

Output:

  Distance  Time  Speed
0       100     2   50.0
1       200     4   50.0
2       300     6   50.0

Explanation: This example constructs a new attribute, "Speed," from existing features. Attribute construction enriches datasets with meaningful variables to improve analysis and model performance.

Also Read: Data Science Vs Data Analytics: Difference Between Data Science and Data Analytics

Data Smoothing Techniques

Applies methods to reduce noise or fluctuations in datasets, revealing underlying patterns and trends.

• When to Use: When data contains high variability or outliers that obscure trends, especially in time-series datasets.
• How to Implement: Use techniques like moving averages, exponential smoothing, or median filtering.
• Pros: Improves clarity of patterns, reduces noise, and prepares data for analysis.
• Cons: May oversimplify data and remove important variability.
• Use Case: Smoothing daily sales data to observe long-term trends for business insights.

Sample Code: Moving Average Smoothing

import pandas as pd

# Sample data
data = {'Sales': [200, 300, 400, 500, 600]}
df = pd.DataFrame(data)

# Apply a 3-period moving average
df['Smoothed_Sales'] = df['Sales'].rolling(window=3).mean()

print("Smoothed Data:\n", df)
</> Copy Code

Output:

  Sales  Smoothed_Sales
0    200             NaN
1    300             NaN
2    400           300.0
3    500           400.0
4    600           500.0

Explanation: This example applies a 3-period moving average to smooth daily sales data. Smoothing reduces short-term fluctuations, making it easier to observe long-term trends in time-series data.

Also Read: Basic Fundamentals of Statistics for Data Science

Sample Code: Exponential Smoothing

import pandas as pd

# Sample data
data = {'Sales': [200, 300, 400, 500, 600]}
df = pd.DataFrame(data)

# Apply exponential smoothing
alpha = 0.5  # Smoothing factor
df['Exp_Smoothed_Sales'] = df['Sales'].ewm(alpha=alpha).mean()

print("Exponentially Smoothed Data:\n", df)
</> Copy Code

Output:

  Sales  Exp_Smoothed_Sales
0    200          200.000000
1    300          266.666667
2    400          342.857143
3    500          426.666667
4    600          516.129032

Explanation: Exponential smoothing gives more weight to recent observations, making it suitable for dynamic time-series data with trends. This method is commonly used in forecasting.

Also Read: Data Visualisation: The What, The Why, and The How!

Now that you have a clearer understanding of why data transformation is essential, let's explore the specific steps involved in this process

What Are the Different Processes for Data Transformation in Data Mining?

Data transformation is a crucial step in the data mining lifecycle, improving data quality and consistency while ensuring compatibility with analytical models. It addresses issues like missing values, inconsistencies, and skewed distributions, paving the way for uncovering valuable insights.

Here are the essential steps in the data transformation process, providing practical guidance on how to approach each phase effectively. Let's break it down:

Step 1: Data Collection and Initial Assessment

Gathering high-quality data from reliable sources and inspecting it for inconsistencies, missing values, or outliers. This step sets the foundation for effective transformation and ensures that data is fit for analysis.

Why It Matters: Helps identify transformation needs, ensuring that the data is suitable for mining. A thorough initial assessment reveals potential issues that may hinder the quality of insights drawn from the data.

Example: Reviewing a dataset for varied date formats or missing values in sales data.

Enhance your understanding of data transformation and machine learning with upGrad’s Artificial Intelligence in the Real World free course. This course complements your studies by providing practical insights and real-world applications, helping you grow your career in AI. Start learning today!

Also Read: Data Science Process: Understanding, Data Collection, Modeling, Deployment & Verification

Step 2: Selecting Appropriate Transformation Techniques

Choosing the right transformation methods based on data types (numerical, categorical), project objectives, and algorithm requirements. This involves matching techniques like normalization, discretization, or feature engineering to the data’s characteristics.

Why It Matters: Ensures transformations align with project goals and enhance data quality. Selecting the right techniques boosts the effectiveness of algorithms by providing them with data that meets their structural needs.

Example: Using normalization for scaling numeric data in clustering algorithms.

Also Read: Top 10 Data Modeling Tools You Must Know

Step 3: Implementing Data Transformation

Applying selected transformation techniques using tools or programming languages like Python Python, R R, or specialized software. This step requires implementing the chosen methods to prepare the data for the analysis phase.

Why It Matters: Transforms data into a structured, analyzable format. Effective implementation ensures that data is ready for accurate modeling and analysis, facilitating better outcomes.

Example: Implementing a logarithmic transformation to stabilize variance in highly skewed datasets.

Also Read: Data Science Life Cycle: Step by Step Explanation

Step 4: Evaluating Transformation Outcomes

Assessing the effectiveness of transformations through metrics like skewness reduction, or visualization techniques such as histograms. This step confirms that the transformations have met their intended goals.

Why It Matters: Ensures the transformed data meets the desired quality and compatibility for mining tasks. Evaluation helps in identifying whether any additional changes are needed to refine the data further.

Example: Using scatter plots to verify that normalization has equalized data ranges.

Also Read: Bar Chart vs. Histogram: Which is Right for Your Data?

Step 5: Iterating and Refining Transformations

Revisiting and fine-tuning transformations to address new insights or improve outcomes. This step is necessary to adapt to changing data patterns or project needs.

Why It Matters: Continuous improvement enhances data quality and mining results. Iterating and refining transformations helps maintain high data quality throughout the data mining process.

Example: Adjusting normalization ranges to better align with new algorithm requirements.

By following these steps, you can transform raw data into actionable insights, optimizing it for data mining processes.

Also Read: Data Mining Techniques & Tools: Types of Data, Methods, Applications [With Examples]

However, to effectively transform data, it's important to choose the right tools and software for the job.

What Are the Best Tools and Software for Data Transformation in Data Mining?

Various tools and software options exist, each catering to different user needs, from coding libraries for developers to user-friendly platforms for business analysts. Below, you will explore key tools and software, highlighting their features, advantages, and ideal use cases.

Scripting Tools: Python and R Libraries

Python and R are popular programming languages with extensive libraries for data transformation. Python offers libraries like Pandas, NumPy, and Scikit-learn, while R has packages like dplyr and tidyr.

Advantages: Highly flexible, enabling custom data transformation scripts; vast community support with extensive documentation and tutorials.

Ideal Use Cases: Suitable for data scientists and developers who require advanced data manipulation and customization capabilities.

Example: Using Pandas to clean and transform a dataset by handling missing values and normalizing columns.

Also Read: R vs Python Data Science: The Difference

Low-Code and No-Code Platforms

These platforms offer visual interfaces that simplify data transformation tasks without requiring extensive coding knowledge. Examples include Alteryx, Microsoft Power BI, and Tableau Prep.

Advantages: Easy to use, reducing the need for technical expertise; accelerates the data transformation process with drag-and-drop functionalities.

Ideal Use Cases: Ideal for business analysts and teams looking for quick data preparation without extensive coding experience.

Example: Using Alteryx to blend data from multiple sources and apply basic transformations to create clean datasets for reporting.

ETL Tools: Talend and Informatica

ETL (Extract, Transform, Load) tools are specifically designed to facilitate large-scale data transformation processes. Talend and Informatica are two prominent ETL platforms.

Advantages: Robust data integration capabilities, scalable for handling large datasets, and support for complex transformation workflows.

Ideal Use Cases: Best for enterprises and organizations that need to process large volumes of data and integrate data from multiple sources.

Example: Using Informatica to automate data pipelines that transform raw data from various sources into a structured format for analytics.

Also Read: Talend Data Integration Architecture & Functional Blocks

Data Visualization Tools with Transformation Capabilities

Data visualization tools like Tableau and Power BI offer built-in data transformation features, allowing users to preprocess data while creating visual reports.

Advantages: Provides a seamless experience for transforming data and simultaneously visualizing it; intuitive and accessible for non-technical users.

Ideal Use Cases: Suitable for teams focused on data exploration and reporting, where transformations are needed for visualization purposes.

Example: Using Power BI to apply filters, data aggregation, and simple transformations to prepare datasets for dynamic dashboards.

These tools and software help streamline the data transformation process, enabling users to choose the right approach based on their skills and project needs.

Also Read: Top 10 Data Visualization Types: How To Choose The Right One?

To fully leverage the power of data transformation in data mining, it's important to understand both its advantages and potential challenges.

What Are the Key Benefits and Challenges of Data Transformation in Data Mining?

Data transformation in data mining plays a crucial role in optimizing data for analysis, offering multiple benefits while also presenting certain challenges. Understanding these aspects helps in maximizing the effectiveness of data transformation efforts and preparing for potential obstacles.

Benefits of Data Transformation in Data Mining

Data transformation in data mining is a critical step in preparing datasets for effective analysis and modeling. By enhancing data quality and enabling compatibility with algorithms, it ensures more accurate insights and reliable outcomes.

• Enhances Data Quality and Consistency: By standardizing and cleaning data, transformations ensure uniformity and reliability, which is essential for accurate analysis.
• Improves Algorithm Performance: Transformed data is often better suited for algorithms, improving the efficiency and accuracy of machine learning models.
• Facilitates Better Data Visualization and Interpretation: Transformations like normalization and scaling make it easier to visualize trends and relationships, aiding in data analysis and decision-making.
• Enables the Handling of Diverse Data Types: Transformations help convert raw data into compatible formats, allowing seamless integration of various data types and sources.

Challenges in Data Transformation

Data transformation in data mining is a powerful step in data mining, but it is not without its hurdles. Overcoming issues like handling vast datasets, ensuring data integrity, and choosing the right techniques requires careful planning and expertise.

• Managing Large Datasets: Working with vast amounts of data can be resource-intensive, requiring efficient tools and techniques to process them effectively.
• Ensuring Data Integrity During Transformation: Ensuring that data remains accurate and consistent throughout the transformation process can be challenging, particularly with complex or automated workflows.
• Selecting Appropriate Transformation Techniques: Choosing the right techniques to apply to different types of data or analysis needs can be difficult and may require deep domain knowledge.
• Balancing Transformation Complexity with Computational Efficiency: Advanced transformations can lead to increased computational demands, impacting processing speed and resource usage. It's crucial to balance complexity with efficiency to avoid performance bottlenecks.

By understanding these benefits and challenges of data transformation in data mining, you can better plan and execute data transformation strategies that align with your project goals and technical constraints.

Also Read: Importance of Data Science in 2025 [A Simple Guide]

With a clear understanding of the benefits and challenges of data transformation, it's time to explore how you can further enhance your skills in this field.

How Can upGrad Help You in Your Data Transformation Journey?

Data transformation is a vital process in data mining that enhances data quality and optimizes models for better decision-making. Essential techniques like normalization, scaling, aggregation, and discretization play a key role in preparing data for analysis. These methods help streamline data processing, making it easier to uncover valuable insights and improve the accuracy of machine learning models.

To further advance your skills in data transformation and machine learning, upGrad’s AI and ML courses offer the perfect opportunity. With expert-led instruction, hands-on projects, and personalized mentorship, you’ll be equipped to overcome any skill gaps and accelerate your career in the fast-growing field of data science.

While the course covered in the article can significantly improve your knowledge, here are some additional free courses from upGrad to facilitate your continued learning:

• Advanced Prompt Engineering Course with ChatGPT
• Introduction to Natural Language Processing
• Object Oriented Analysis and Design for Beginners
• NodeJS for Beginners

You can also get personalized career counseling with upGrad to guide your career path, or visit your nearest upGrad center and start hands-on training today!

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Reference links:
https://www.statista.com/statistics/871513/worldwide-data-created/

References:

• https://www.k2view.com/what-is-synthetic-data-generation/
• https://arxiv.org/abs/2405.09592