Top 10 Data Science Algorithms Tech Enthusiasts Should Know

by Sayantani Sanyal
April 30, 2022

Data science algorithms will help professionals execute their tasks efficiently and effectively

Data science has become the backbone of some of the most advanced enterprise applications in the world. The field has quickly become one of the most in-demand professions in the market, with multinational corporations and small businesses around the world on the hunt for data scientists and skilled data professionals. Data science remains one of the most essential techniques in almost all situations. Data professionals are crucial for maximizing the organization’s time, resources, and labor. But to excel in the domain, aspiring professionals should learn the various data science algorithms that will help them perform that analysis and prediction tasks with ease and efficiency. The top data science algorithms will help perform complex data science tasks like prediction, classification, clustering, and others. In this article, we have listed the top most popular data science algorithms that tech enthusiasts and data professionals should definitely know about in 2022.

Linear Regression

The linear regression method is extensively used for predicting the value of the dependent variable by using the values ​​of the independent variables. This algorithm is mainly used to understand the linear relationship between the input and the output variable. It is represented in the form of a linear equation which has a set of inputs and a predictive output.

Logistic Regression

Logistic regression is used for the binary classification of data points. It performs a categorical classification that results in the output belonging to either of the two classes (1 or 0). The two most crucial parts of this algorithm are the Hypothesis and the Sigmoid Curve.

K-Means Clustering

K-means clustering is a type of unsupervised machine learning algorithm. Clustering basically means dividing the data set into groups of similar data known as clusters. K means clustering categorizes the data items into k groups with similar data items.

Principal Component Analysis

PCA is basically a technique for performing dimensionality reduction of the datasets with the least effect on the variance of the datasets. This indicates removing the redundant features but keeping the important ones.

Decision Tree

Decision Tree algorithm in machine learning is one of the now popular algorithms that is extensively used for data science purposes. This is a supervised learning algorithm that is generally used for classifying problems. It works well in classifying both categorical and continuous dependent variables.

Naive Bayes

A naive bayes classifier assumes that the presence of a particular feature in one class is unrelated to the presence of any other feature. Even if these features are all related to each other, this classifier algorithm would consider these properties independently when calculating the probability of a particular outcome.


The KNN is another algorithm that can be applied to both classification and regression problems. The data science domain is widely used to solve classification problems. This easy and simple model can be used to store all available cases and classifies any new cases by taking the majority vote from the neighbors.

Random Forests

Random forests aids in overcoming the problems created by decision trees and help in solving both classification and regression issues. It works on the principle of ensemble learning, which beliefs that a large number of weak learners can work together for giving high accurate predictions.

Support Vector Machines

SVM is a supervised algorithm that is used for data science classification issues. The algorithm tries to draw two lines between the data points with the largest margin between them. The user needs to plot the data items in n-dimensional space, where the n is the number of input features. Based on this, the SVM algorithm separates the possible outputs by their class label.

Artificial Neural Networks

Neural networks are modeled after the neurons in the human brain. It comprises several layers of neurons that are structured to transmit information from the input layer to the output layer. A simple neural network comprising a single hidden layer is called a perceptron.

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