Analytics

Getting to know the types of data science algorithms and their performance

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We cannot deny that data science is one of the important fields in this era of digital transformation. This is because most companies need data to aid in the decision-making process. In its application, data science uses algorithms to solve a problem in a business process. Basically, data science is not a science that stands alone. Rather, it is a combination of several disciplines such as statistics, mathematics and computers.

In practice, data science combines machine learning and artificial intelligence (AI) to form an algorithm. This algorithm will be useful later in the decision making system of the company.

There are many different types of data science algorithms that companies often use to help their business processes. Usually, companies use algorithms according to the type of data they have in the database.

For more details, see the description through the article below.

Types of data science algorithms

Algorithm A type of algorithm is a sequence of logical steps that are useful for systematically solving a problem. In simple words, we can define an algorithm as a set of instructions that are structured and implemented in the form of a computer program to solve a specific computational problem.

In the business world, this algorithm will be very useful to help with data processing (data management) and data analysis. Especially the analysis of large volumes of data (big data analysis). With data science algorithms, computational processes run faster, more efficiently, and deliver accurate results to support business growth. To implement it, there are several types of algorithms that data scientists must master.

In general, this algorithm is divided into three categories based on the type of data available. namely supervised learning, unsupervised learning and reinforcement learning. Below is an explanation of each:

Supervised learning algorithm (classification models)

A unified learning algorithm, where the algorithm uses data that has labels. That is, supervised learning algorithms identify features explicitly for the prediction and classification process. Supervised learning algorithms are divided into two types of training data and test data.

Therefore, this type of algorithm cannot learn by itself, but must first receive an example. The trick is to label the dataset. Supervised learning can help companies solve various problems. For example, classifying spam in a separate folder in the email inbox.

On the other hand, this algorithm has 3 models. That is, classification, regression and prediction. Examples of supervised learning algorithms that are very popular are Naive Bayes Classifier, K-Nearest Neighbor (KNN), Linear Regression, Random Forest, Decision Tree and Artificial Neural Network (ANN).

Unsupervised learning algorithm (cluster analysis).

Next is unsupervised learning or cluster analysis. Unlike supervised learning, this type of algorithm does not need to learn monolithically. In other words, unsupervised learning uses data that has no labels. This algorithm identifies data based on structure, similar parts, density and similar features. The point is to draw conclusions from the data set.

This algorithm studies data only based on similarity or it can be called clustering. The purpose of this clustering is to group the data, so that the objects in the same cluster are similar. With clustering, companies can identify market segments or segment potential customers (qualified leads) to become a sales target market.

Reinforcement learning algorithm

The third type is reinforcement learning, which is also part of the deep learning method. This algorithm is different from supervised learning and unsupervised learning. Because the purpose of this algorithm is that computers can automatically learn from the environment.

Reinforcement learning is usually useful to help discover situations that require action. Or help you figure out which action plans are the most rewarding in a given period.

In reinforcement learning algorithm, there are several important terms such as agent, environment (e), reward (r), state (s), policy (π), value (V), value function, environment model, model based model. . Methods and Q value or action value (Q).

  • Agent: An entity that performs an action in the environment to obtain some reward
  • Environment (e): The scenario that the agent has to deal with
  • Reward (r): an immediate return to the agent when a particular action or task is performed
  • State: A state that refers to the current state
  • Policy ( ): The strategy that the agent applies to decide the next course of action based on the situation
  • Value (V): Long-term return
  • Value function: A function that determines the state value, which is the total number of rewards
  • Environment Model: A modeling process whose task is to simulate the state of the environment
  • Model-based method: A problem-solving method that uses a model-based method
  • Q value or action value (Q): Almost the same as value (V), but requires additional parameters for action

An example of the use of reinforcement learning is a robot that moves goods from one place to another in a manufacturing industry. This robot is trained to remember objects and perform tasks with high accuracy and speed.

An example of reinforcement learning algorithm is Q-Learning. This is an overview of data science algorithms. Basically, data science itself plays an important role in the company’s growth and development strategy. Especially in the midst of the current digitalization of business.

Meanwhile, you can also use the services of a digital marketing agency to help develop your business. Some of the strategies that can be implemented are growth hacking marketing, inbound marketing, 360 digital marketing and data driven marketing so that the company can grow quickly.

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