Data Science Algorithms – Aspirants Must Know


There are some computer science algorithms which are used very commonly in the field of data science. Every aspiring data scientist should be well-versed with each and every one of these algorithms.

So in this article we will present a list of “Data science algorithms which every data science aspirant must know.”

Data Science Algorithms

1. Linear regression -

Linear regression is a data science algorithm which employs the techniques of graphing and algebraic variables. The core concept of how linear regression works is quite simple, but since the implementation can become quite complex and involved, it is good to explain the algorithm as clearly as possible. Most data science courses have a module on this algorithm.

First two variables are chosen. The first variable is called the variable x and the second variable is called the variable y. Then the data scientist studies how the value of variable x is changing with respect to the value of the variable y.

Sometimes it is the other way round too. By studying the changes in the values of the two variables, the data scientist is able to draw an advanced and sophisticated graph which shows the change in the values of the two variables in a clear and visually appealing visual representation.

By following the plot of the graph the data scientist is able to predict what kinds of changes will take place in the values of the two variables, variable x and variable y, in the future.

2. Logistic regression -

We have explained how the algorithm of linear regression works in the previous part of the article. Now we will explain how the algorithm of logistic regression works and try to understand its mechanism clearly.

The algorithm of logistic regression is a significantly more advanced, sophisticated, and complex algorithm than the algorithm of linear regression.

The algorithm of logistic regression uses a lot of logarithmic and statistical techniques to accomplish its goals. Similar to the algorithm of linear regression, logistic regression is used to predict the changes which will occur in the variables being studied.

This kind of study can prove to be very helpful in a lot of fields and domains. Many data science courses teach statistics and logarithms before teaching this algorithm.

For instance, one can use logistic regression to calculate the chances of a particular political party winning the elections in a particular year. Or one can calculate the chances of a particular high school student being selected in a particular college.

The applications of logistic regression are too numerous to list comprehensively in one article. This algorithm performs a central function in many scientific and mathematical calculations and computations of the world.

The word logistic can give us a hint about what the applications of this algorithm might be in the real world. But actually this word is very misleading as this algorthim does not have anything to do with supply chain management or any kind of resource or asset management.

3. Gradient descent -

Gradient descent is a very interesting algorithm used very commonly in data science. The core concepts involved in gradient descent are drawn from calculus and graphing functions.

At its core, gradient descent is an optimization algorithm. This means that it is used to find the optimal pathway between any two points on a function graph.

The gradient algorithm is most commonly used to minimize the costs involved in any business process or operation. That is the most common application of the gradient descent algorithm.

In addition to that it is also used to find the most efficient route between any two points in a spatial map or to find the most efficient way to use a given amount of resources. This efficiency is discussed in many data science courses.

With the advent of cloud computing gradient descent can be applied on a widespread scale. The algorithm of gradient descent is a very resource intensive one and so it needs a lot of computing power in order to be executed properly.

With cloud computing data centers connected by internet networks, the load of processing data through the gradient descent algorithm can be spread out evenly and can be handled elegantly and with a minimum of inconvenience.

4. KNN -

The full form of KNN is K-Nearest Neighbours. It is a very popular and a very effective algorithm used in the fields of data mining and machine learning. As an algorithm, it can be put under the category of supervised machine learning.

This means that while there is no outside intervention by the programmer to teach the machines how to learn from their environment and make decisions based on the information that they collect, there is still some contribution from the programmer’s side at the level of the codebase of the machine.

KNN is a pathfinding algorithm. The central principle of this algorithm is to find the nearest neighbours with respect to the location of a certain point.

These neighbours exist in the form of vectors or coordinates and if the shortest path between two or more neighbours can be mapped it can be of great use for data scientists.

The applications of the KNN algorithm are many and varied. It finds use in a large number of domains and fields. There are many classes of problems which are rendered solvable with the use of the KNN algorithm.

It can be used to solve problems related to managing traffic, predicting airplane chokepoints, and even in designing strategies for football teams. The solutions to all these problems are discussed in the data science course.

5. Decision tree -

The decision tree algorithm is a very common algorithm in data science. It is also a supervised machine learning algorithm which is used in the fields of data mining and machine learning.

As the name suggests, it is used to help machines make decisions based on the information that they collect from their environment.

It treats data as a tree of different leaves or nodes and navigates between the different nodes based on some conditions set by the programmer.

If a particular node satisfies those conditions, the decision tree algorithm proceeds down that node. In this way, it makes its way through any particular path. One can learn how to set these conditions by getting a Data Science certification.

6. Clustering analysis -

The algorithm of cluster analysis works in a very subtle way. The data scientist groups data into various segments called clusters. These clusters are groupings of data of a similar nature.

So the result of this grouping is that data of a similar nature is grouped into the same segment and data across different segments is different in nature.

One can learn how to divide the data into segments by getting a Data Science certification.

7. Naive Bayes -

The Naive Bayes algorithm is based on the theory of probability. It makes use of several probabilistic techniques in order to classify data into different segments based on the probability of them occurring in the data set again.

The most common application of the Naive Bayes algorithm is in detecting and predicting spam emails.

One can learn this application of the Naive Bayes algorithm by getting a Data Science certification.

8. SVM (support vector machine) -

Support vector machine or SVM is a kind of supervised machine learning algorithm. It is based on very sophisticated and advanced principles of computer science. It involves creating a kind of classifier for data known as a hyperplane.

The data is stored in this hyperplane in a continuum so that there is no distinguishable difference between the various data points.

One can learn how to create a hyperplane by getting a Data Science certification.

9. K-means clustering -

K-means clustering is an algorithm which has a very specific and a very unique goal. It is used when the data scientist has to deal with unlabelled data or data which is not clearly classified and divided into groups.

The goal of the K-means clustering algorithm is to separate this data into numbered groups with each group having a similar kind of data.

One can learn how to divide data into numbered groups by getting a Data Science certification.

10. Random forests -

A random forest can be thought of as a group of decision trees. It is generally observed that a group of decision trees make much more accurate predictions than a single decision tree because in a group the algorithm has to jump through many more conditions.

One can learn how to program these conditions into the code of the machines by getting a Data Science certification.


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