We cannot deny that data science is one of the important fields in the era of digital transformation like now. This is because the majority of companies need data to help the decision-making process. In its application, data science uses algorithms to solve a problem in business processes. Basically, data science is not a stand-alone science. Rather it is a combination of several fields such as statistics, mathematics, and computers. In practice, data science will combine machine learning and artificial intelligence (AI) to form an algorithm. It is this algorithm that will later be useful in the company’s decision-making system.
There are several types of data science algorithms that companies often use to help the company’s business processes. Usually, companies will use algorithms according to the type of data they have in the database. For more details, see the explanation through the following article.
Algorithm Type Data Science Algorithm is a logical sequence of steps that are useful for solving a problem systematically. In simple terms, algorithms can be interpreted as a set of commands that are structured and implemented into the form of computer programs to solve a certain computational problem. In the business world, this algorithm will be very useful to help the process of data processing (data management) and data analysis, especially analyzing large amounts of data (big data analytics). With data science algorithms, computational processes will run faster, more efficiently, and provide accurate results to support business growth.
For its application, there are several types of algorithms that data scientists need to master. In general, this algorithm is divided into three based on the type of data that exists. Namely supervised learning, unsupervised learning, and reinforcement learning. Here are the explanations for each of them:
1. Supervised Learning Algorithm (Classification Models)
It is a unified learning algorithm, where this algorithm uses data that has labels. That is, supervised learning algorithms identify features explicitly for the prediction and classification process. digital marketing agency The supervised learning algorithm is divided into two types, namely training data and test data. Thus, this type of algorithm cannot learn on its own, but must first get an example. The trick is to label the dataset.
Supervised learning can help companies to solve various problems. For example classify spam in a separate folder in the email inbox. On the other hand, this algorithm has 3 models. Namely classification, regression, and forecasting. Examples of supervised learning algorithms that are quite popular are Naive Bayes Classifier, K-Nearest Neighbour (KNN), Linear Regression, Random Forest, Decision Tree, and Artificial Neural Network (ANN).
2. Unsupervised Learning (Clustering Analysis) Algorithm Next is unsupervised learning or clustering analysis. Different from supervised learning, this type of algorithm does not require integrated learning. Arinya, unsupervised learning uses data that does not have a label. The algorithm will identify data based on the same structure, similar segments, density and features. The point is to draw conclusions from the dataset. This algorithm will only study a data based on its similarity or we can call it clustering.
The purpose of this clustering is to group data, so that the objects on one cluster will be similar. With clustering, companies can identify market segments or segment potential customers (qualified leads) to become sales target markets. Through this grouping, companies can determine which customer segments have the most potential to buy products in the sales process. Thus, this is useful for increasing the company’s sales growth, revenue, and profitability. I
n the process, clustering consists of a number of stages. Namely identifying the object of study as a cluster. It then groups the clusters by similarity to form larger clusters. Then the new data analyst can analyze it. Some examples of unsupervised learning algorithms are K-Means, DBSCAN and hierarchy clustering.
3. 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 this algorithm aims to make computers able to learn from the environment automatically. Reinforcement learning is usually useful to help discover which situations require action. Or help find which action plan earns the highest reward during a certain period.
In reinforcement learning algorithms, there are several important terms, namely agent, environment (e), reward (r), state (s), policy (π), value (V), value function, model of the environment, model based methods, and Q value or action value (Q).
- Agent : an entity that performs an action in the environment to get some reward
- Environment (e) : scenarios that the agent must face
- Reward (r) : direct return given to the agent when performing a certain action or task
- State : state that refers to the current situation
- Policy (π) : the strategy that the agent applies to decide the next action based on state
- value(V) : long-term return
- Value function : function that determines the state value which is the total amount of reward
- Model of environment : model process in charge of imitating the state of the environment
- Model based method : problem solving method that uses the model-based method
- Q value or action value (Q) : almost similar to value (V), but requires additional parameters for the action
An example of the use of reinforcement learning is a robot to take goods from one place to another in the manufacturing industry. The robot is trained to memorize objects and perform work with high precision and speed. An example of a reinforcement learning algorithm is Q-Learning. Thus is a review of data science algorithms. Basically, data science itself does play an important role in the growth strategy and development of the company. Especially in the midst of the rampant digitalization of business as it is today.
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