15 common data science techniques to know and use

How data science finds relationships between data

When identifying information needles in data haystacks, data scientists must discern how different data elements relate. Imagine there's a bunch of data points plotted on a graph. These points could mean the following:

• The data represents a relationship between two or more variables that's best described by plotting a line or multidimensional plane.
• The data represents clustered groups that have some affinity.
• The data represents different categories.

Determining these relationships gives meaning to otherwise random data. Data scientists can then analyze and visualize the data to provide organizations with the information they need to make decisions or plan strategies. To do so, they use the classification, regression and clustering techniques detailed below.

Classification techniques

Data scientists are looking to answer a primary question regarding classification problems: What category does this data belong to?

There are many reasons for classifying data. If the data is a handwriting image, you might want to know what letters or numbers it represents. If the data represents loan applications, you may want to determine whether they should be approved or declined. Other classifications focus on determining patient treatments or whether an email message is spam.

Data scientists use the following algorithms and methods to filter data into categories.

• Decision trees. This is a branching logic structure using machine-generated trees of parameters and values to classify data into defined categories.
• Naïve Bayes classifiers. Using probability, Bayes classifiers help put data into simple categories.
• Support vector machines. SVMs draw a line or plane with a wide margin to separate data into different categories.
• K-nearest neighbor. This technique uses a simple "lazy decision" method to identify the category a data point should belong to. This decision is based on the categories of its nearest neighbors in a data set.
• Logistic regression. This classification technique fits data to a line to distinguish between different categories on each side. The line's shape is such that data shifts between categories rather than allowing for more fluid correlations.
• Neural networks. This approach uses trained artificial neural networks, particularly those employing deep learning with multiple hidden layers. Neural nets have shown profound classification capabilities with extensive training data sets.

Regression techniques

Instead of trying to find which category data falls into, teams might like to know the relationship between data points. Regression aims to find the predicted value of the data. It comes from the statistical idea of "regression to the mean."

Regression can either be straightforward -- between one independent and one dependent variable -- or multidimensional, which tries to find the relationship between multiple variables.

Some classification techniques, such as decision trees, SVMs and neural networks, can also do regressions. Other regression techniques include the following:

• Linear regression. One of the most widely used data science methods, this approach tries to find the line that best fits the analyzed data based on the correlation between two variables.
• Lasso regression. This technique improves the prediction accuracy of linear regression models by using a subset of data in a final model. Lasso is short for "least absolute shrinkage and selection operator."
• Multivariate regression. This technique involves identifying lines or planes that align with multiple data dimensions, potentially containing several variables.

Clustering and association analysis techniques

Clustering and association analysis help data scientists determine how data forms into groups and which groups different data points belong to.

Clustering

Clusters of related data points share various characteristics. They provide valuable insights for analytics applications. Methods for clustering and their uses include the following:

• K-means clustering. A k-means algorithm determines a certain number of clusters in a data set and finds the centroids, which identify cluster locations. Data points are assigned to the closest one.
• Mean-shift clustering. This is another centroid-based clustering technique. Using it alone is possible, but it can also improve k-means clustering by shifting the designated centroids.
• DBSCAN. This technique for discovering clusters uses a more advanced method of identifying cluster densities by grouping data points and marking outliers as noise. DBSCAN is short for "Density-Based Spatial Clustering of Applications with Noise."
• Gaussian mixture models. GMMs find clusters using a Gaussian distribution to group data together rather than treating the data as singular points.
• Hierarchical clustering. Similar to a decision tree, this technique uses a hierarchical, branching approach to find clusters.

Association analysis

Association analysis is a related, but separate, technique. It finds association rules that describe commonalities between different data points. Like clustering, it finds groups that the data belongs to.

However, in clustering, the goal is to segregate a large data set into identifiable groups. Association analysis measures the degree of association between data points. It tries to determine when data points will occur together, rather than identify clusters after the fact.

Data science application examples

Apply the prior methods and techniques to specific analytics problems, questions and the available data to address them. Good data scientists understand the nature of the issue at hand -- clustering, classification or regression -- and the best algorithmic approach to yield desirable answers given the data's characteristics. This is why data science is a scientific process, rather than a set of hard and fast rules.

Using these techniques, data scientists can tackle various applications, many of which are common across different industries and organizations. Here are a few examples.

Anomaly detection

Identifying the pattern for expected -- or "normal" -- data makes it easier to find data points that don't fit the pattern. Companies in diverse industries, such as financial services, healthcare, retail and manufacturing, regularly employ various data science methods to identify anomalies in their data. Use cases include fraud detection, customer analytics, cybersecurity and IT systems monitoring. Anomaly detection can also eliminate outlier values from data sets for improved analytics accuracy.

Binary and multiclass classification

A primary application of classification techniques is determining if data belongs to a particular category. This is known as binary classification. A practical business application uses image recognition to identify contracts or invoices among piles of documents.

In multiclass classification, data scientists want to identify the best fit for data points among many categories in a data set. For example, the U.S. Bureau of Labor Statistics uses it for automated classification of workplace injuries.

Personalization

Organizations looking to personalize interactions or recommend products and services must first group individuals into data buckets based on shared characteristics. Effective data science enables organizations to tailor websites, marketing offers and more to individuals' specific needs and preferences. They can achieve this using recommendation engines and hyper-personalization systems that match data in people's detailed profiles.

Editor's note: This article was updated by TechTarget editors for timeliness in November 2025.

Ron Schmelzer is the founder of Scalebrate and publisher of the Exponential Scale newsletter and podcast.