Key data science modeling techniques used in data evaluation and analysis

data science modeling techniques

We always talk about how data analytics platforms can generate the necessary insights organisations need to optimise business operations. But, we seldom dive into the modeling techniques data analysts use to breakdown data and generate useful insights.There are several modeling techniques at an analyst’s disposal, but in the interest of time, we are only going to cover the most essential data science modeling techniques, along with some crucial tips to optimise data analysis.

Key data science modeling techniques used

There are several data science modeling techniques data analysts use, some of which include:

Linear regression

Linear regression is a data science modeling technique that predicts a target variable. It completes this function by finding the “best” relationship between the independent and dependent variable. The resultant graph should ideally ensure that the sum of all the distances between the shape and the actual observation is small. The smaller the distance between the mentioned points, the smaller the chances of an error occuring.

Linear regression is further divided into the subtypes: simple linear regression and multiple linear regression. The former predicts the dependent variable using a single independent variable. Meanwhile, the latter uses the best linear relationship by using several independent variables to predict the dependent variable.

Non-linear models

Non-linear models are a form of regression analysis using observational data modeled by a function. It is a nonlinear combination of model parameters and depends on one or more independent variables. Data analysts often use different options when handling non-linear models. Techniques like step function, piecewise function, spline, and generalised additive model are all crucial techniques in data analysis.

Supported vector machines

Supported vector machines (SVM) are data science modeling techniques that classify data. It is a constrained optimisation problem with a maximum margin found. However, this variable depends on the restrictions that classify data.

Supported vector machines find a hyperplane in an N-dimensional space that classifies data points. Any number of planes could separate data points, however, the key is to find the hyperplane that has the maximum distance between the points.

Pattern recognition

You may have heard of this term in the context of machine learning and AI, but what does pattern recognition mean? Pattern recognition is a process where technology matches incoming data with the information stored in the database.

The objective of this data science modeling technique is the discovery of patterns within the data. Pattern recognition is different from machine learning because the former is a subcategory of the latter.

Pattern recognition often takes part in two stages. The first is the explorative part, where the algorithms look for patterns without a specific criteria. Meanwhile, the descriptive part is where the algorithms categorise the discovered patterns. Pattern recognition can analyse any type of data, including texts, sound, and sentiment.


Resampling methods refer to data science modeling techniques that consist of taking a data sample and drawing repeated samples from it. Resampling generates unique sampling distribution results, which could be valuable in analysis. The process uses experiential methods to generate a unique sampling distribution. As a result of this technique, it generates unbiased samples of all the possible results of the data studied.


Bootstrapping is a data science modeling technique that helps in different scenarios, like validating a predictive model performance. The method works by sampling a replacement from the original data with certain data points that are not used as test cases. By contrast, there is another method called cross validation, which is a technique used to validate model performance. It works by splitting the training data into different parts.

Tips to optimise data science modeling

Most of the data science modeling techniques are crucial for data analysis. However, along with these data analysis models, there can be several viable techniques used to optimise the data science modeling process.

For example, data visualisation technology can go a long way in optimising the process. Staring at rows and columns of alphanumeric entries makes it difficult to conduct any meaningful analysis. Data visualisation can make the process much easier by converting all alphanumeric characters into graphs and charts.

The right data analytics platform can also play a huge role in optimal data analysis. With optimised data analytics platforms, it can increase the rate of data analysis, delivering insights at an even faster rate.

This is where Selerity can help! We have a team of SAS experts that can provide administration, installation, and hosting services to help you optimise your data collection and analysis.

Visit Selerity to know more information on data science modeling techniques.


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