5 Main Data Science Algorithms that a Data Scientist Should Know

Here are the five universally used data science algorithms.

Data science is a field where decisions are made by analyzing data to get insights rather than methods that are based on several principles. In general, a machine learning task can be divided into three sectors:

1. Obtaining the data and mapping the business problem.

2. Applying machine learning techniques and analyzing the metrics.

3. Testing and finalizing the model.

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But data science algorithms can also solve machine learning tasks. While there are many algorithms out there, here are the five main data science algorithms that are powering the machine learning world.

Types Of Data Science Algorithms

1. Linear Regression:

This one is the most famous data science algorithm. Linear regression finds a line that fits the scattered data points on the graph. It shows the connections between independent factors and a numeric result. That line will then be able to anticipate the values. The most popular procedure for Linear Regression is the least of squares. The end goal of this procedure is to calculate the best fitting line in such a way that the vertical distance from every data point of the line is least. The whole idea is to fit a model by limiting separation between the squares.

2. Logistic Regression

Similar to linear regression, this data science algorithm is used when the output is binary (at the point when the result can only have two values). An exception for this is a non-linear S-shaped function known as the logistic function, g().

This function maps the middle-of-the-road result values to a result with variable Y, which has values extending from 0 to 1. These values can calculate the likelihood of the occurrence of the variable Y. The properties of this S-shaped logistic regression can improve the calculated relapse for the classification tasks.

3. Support Vector Machines

This is an excellent classifier for grouping binary data. Super vector machines are also used in facial recognition and genetic characterization. This algorithm has a pre-assembled regularization model that allows data science professions to minimize the classification errors. This results in expanding the geometrical edge which is a significant aspect of a support vector machine classifier.

This type of data science algorithm has the ability to outline the input vectors to n-dimensional space by building an extreme division hyperplane. The first built hyperplane has two other hyperplanes on either side to measure the distance from the main hyperplane to the other hyperplanes.

4. K means Clustering

This is the universally used unaided clustering calculation method. With a lot of data points as vectors, clusters can be made of the point depending on the distance between them. You can say this is an expectation-maximization algorithm that moves the focus points of the clusters and then clubs the points with each cluster center. The input this algorithm assumes is the number of clusters that are yet to be produced and the amount of iterations needed to combine the clusters.

5. Recurrent Neural Networks

This algorithm is used to learn sequential data. These sequential problems contain cycles that use fundamental time-steps. To process this data, ANNs need a different memory cell to store the data that were the result of the past steps. The data used is what is represented in a progression of time-steps. Hence, this algorithm becomes ideal to deal with problems related to text processing.

Because of their varied uses, these five data science algorithms are the most used in everyday data science tasks. With the knowledge of these algorithms, you are prepared to make a move in the world of data science and machine learning, with some training too, of course.