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Data science technology is a process in which the data is extracted from different sources and then it is analyzed for making predictions and other useful conclusions. This is how we can define a data science course in bangalore briefly. But what strategy does data science follow for executing and accomplishing all of these tasks? We all know that for bringing perfection to a task, we follow a strategy to execute it. Data science also follows some strategies and these strategies in technical terms are known as algorithms. Data science follows many algorithms and the same algorithms are followed in machine learning systems. Those who are interested in machine learning can read further about these algorithms.


Names of the algorithms which are used in data science as well as in machine learning are listed below:

  • Linear Regression
  • Principal component analysis
  • Apriori
  • Decision trees
  • Logistic regression
  • K-means clustering
  • Artificial neural networks
  • Recurrent neural networks
  • Support vector machines


Linear regression is generally used to measure or observe the relationship between two variables. Here the variables are nothing but real-world objects. There are two types of variables present: one is independent and the other is dependent. The independent variable is also known as a predictor. The linear regression is based on the concept of the straight line. Moreover, the relationship between the two variables is also decided with the help of the general equation of the straight line.


With the help of logistic regression, we classify the data in binary classification. This means the result we get after performing the logistic regression would be either 0 or 1. This simply means that an event will either occur or will not occur, for example, if we wanted to find out whether it will rain today or not. We would use logistic regression because we know that the answer or the result would be either yes or no. Moreover, the left and right hand of the logistic regression are a sigmoid curve and the hypothesis.


As we can recognize somewhat from its name, the algorithm forms the clusters of K numbers. In this algorithm, a group of some elements, let’s say for example, the number of elements in the group is n. A group of n number of elements is divided into K number of clusters. This is an iterative algorithm which means the process is repeated certain times. On what basis is this classification done? The classification is done on the basis of the mean. Each n element is placed in that cluster that has its nearest mean. In simple words, the algorithm divides a group of n elements in several subgroups and the classification is done on the basis of the similarity of the elements. K-means clustering is known as the best form of unsupervised learning. Moreover, the algorithm is easy to implement as well as to understand.

Those students who are interested in acquiring knowledge in the data science course and wish to become a data scientist in the future should join a data science training in bangalore.

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360DigiTMG – Live Data Science, Data Analytics Courses in Bangalore

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