Data preprocessing is critical in data science and analytics, especially when building predictive models. Two of the most popular techniques for data preprocessing are normalisation and standardisation. These methods are essential for scaling data and ensuring it is suitable for analysis, making them integral parts of a data analyst course in Pune. While both techniques are used to adjust the values of a dataset, they serve different purposes and are appropriate in distinct scenarios. Understanding when to use normalisation vs standardisation can significantly impact the success of data analysis and model performance.
What is Data Normalization?
Normalisation is a data scaling technique that adjusts data values to fit within a specific range, often between 0 and 1 or -1 and 1. This technique commonly applies to datasets measuring features on different scales and units. By converting features to a similar range, normalisation ensures that no single feature dominates the model due to its larger range or units. This technique is often taught in a data analyst course in Pune as it is critical for algorithms sensitive to data magnitudes, such as k-nearest neighbours (KNN) and neural networks.
Types of Normalisation
- Min-Max Scaling: The most commonly used normalisation method scales data within a defined range, usually between 0 and 1.
- Decimal Scaling: This method moves the decimal point of values based on the range of the data, which is useful for data that varies widely in magnitude.
When to Use Normalisation?
Normalisation is typically beneficial for non-linear and distance-based models, such as KNN and k-means clustering. Since these models rely heavily on distances between data points, normalisation helps ensure that all features contribute equally to the distance calculation. A data analyst course in Pune often emphasises that normalisation is especially useful when data has varying units or scales, as it brings all features to a common scale, making distance-based calculations more meaningful.
Advantages of Normalisation
- Reduced Influence of Outliers: By scaling all features within a defined range, normalisation minimises the impact of extreme values, preventing them from skewing the results.
- Improved Model Performance: Normalisation is particularly effective for algorithms prioritising feature magnitude, often leading to improved accuracy and performance.
Example: Suppose you have a dataset with features like age and income, where age ranges between 20 and 80, while income ranges between 20,000 and 120,000. Using normalisation, you can bring both features to a similar scale, ensuring the model considers both features equally—key learning in a data analyst course.
What is Data Standardization?
On the other hand, standardisation involves rescaling data to have a mean of 0 and a standard deviation of 1. This technique transforms data to follow a standard Gaussian (normal) distribution, which is valuable for algorithms that assume normally distributed data, such as linear regression and principal component analysis (PCA). Standardisation is widely covered in a data analyst course as it prepares data for linear-based models by ensuring they operate efficiently on data with standardised scales.
Types of Standardisation
- Z-Score Standardization: This method converts each value by subtracting the mean and dividing by the standard deviation, giving each feature a mean of 0 and a standard deviation of 1.
- Mean Normalisation: This scales data between -1 and 1 based on the mean and range of the data but is less commonly used than Z-score standardisation.
When to Use Standardisation?
Standardisation is especially suitable for linear models or models that rely on a normal data distribution, such as linear regression, logistic regression, and PCA. In a data analyst course, you would learn that standardisation is ideal when features are on different scales, and the data distribution must be balanced around a zero mean. This technique is particularly effective for data with significant outliers since it ensures that large values do not disproportionately influence the model.
Advantages of Standardisation
- Facilitates Gaussian Distribution: Standardisation makes data more Gaussian-like, which improves model performance when normality is an assumption.
- Handles Outliers Better: Unlike normalisation, standardisation is less affected by outliers, making it more suitable for models sensitive to distribution shape.
Example: If a dataset includes features like temperature, rainfall, and wind speed, each with different scales, standardisation will ensure these features have equal influence on models like logistic regression, making it a crucial concept in a data analyst course.
Comparing Normalisation and Standardisation
When deciding between normalisation and standardisation, it’s essential to consider the data distribution, the type of model being used, and the scale of feature values. Let’s break down some differences:
| Aspect | Normalisation | Standardisation |
| Scale | Specific range (0-1 or -1 to 1) Mean of 0 | standard deviation of 1 |
| Sensitive to | Feature scale and range | Data distribution and shape |
| Ideal for Models | Non-linear models, distance-based | Linear models, normally distributed |
| Outlier Handling | Less effective | Better suited |
In a data analyst course in Pune, you would dive deeper into these distinctions and learn how to apply each technique based on model requirements and data properties.
Choosing the Right Approach
Selecting between normalisation and standardisation depends on multiple factors, including model type, data distribution, and scaling requirements. Here are some guidelines:
- For Distance-Based Models: Use normalisation if your dataset includes features with varied scales. KNN and k-means clustering, for example, works best when data is scaled to a uniform range.
- For Linear Models and Gaussian Distribution: Standardization is preferable for models like linear regression and PCA, which assume normally distributed data. You ensure the model’s assumptions hold by transforming the data to a mean of 0 and a standard deviation of 1.
- When Dealing with Outliers: Standardization is generally more robust in handling outliers as it centres data around zero, reducing the influence of extreme values. This is a key point covered in a data analyst course in Pune.
Practical Considerations and Final Thoughts
When working with real-world data, the choice between normalisation and standardisation is rarely straightforward. Testing both methods is often required to determine which approach yields the best results. For instance, normalisation might produce better outcomes with neural networks due to the limited range, while standardisation might benefit algorithms like PCA, which work best with normally distributed data. A data analyst course in Pune offers practical insights into these methods, allowing students to apply scaling techniques effectively.
In summary, data normalisation and standardisation are indispensable tools in a data analyst’s toolkit. They enable efficient model training, improved accuracy, and consistency in results across diverse datasets. By selecting the right scaling technique, analysts can ensure optimal performance and make data-driven insights more robust and reliable.
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