Data Science is a term that is becoming quite popular nowadays. But what does this mean and what kind of skills do you need? In this article, we are going to answer these questions and find important information. Read more.
First, let’s see what the term refers to. Data science is in fact a combination of many tools, machine learning techniques and algorithms. They are combined to discover hidden patterns based on the given raw data.
First, data science is used to make important predictions and decisions through the use of machine learning, prescriptive analysis and informal analysis. Let’s get a deeper insight.
Predictive Casual Analytics: If you need a model that can predict the course of a certain event further down, you should use this approach. For example, if you are offering money on credit, you may be concerned about getting your money back from debtors. So you can develop a model that can perform a predictive analysis to find out if they will pay on time.
Prescribing analysis: If you need a model that has the ability to make decisions and change them with dynamic parameters, we recommend performing a prescriptive analysis. It has to do with giving advice. So it predicts and suggests many prescribed actions and the associated results.
If you want an example, consider Google’s self-driving car. The data collected by the vehicle can be used to further train these cars. You can also use many algorithms to add more intelligence to the system. This allows your car to make important decisions such as cornering, taking the correct paths and accelerating or decelerating.
Machine learning: Machine learning is another technique used in data science to make predictions. If you have access to a certain type of transaction data and you need to develop a model to predict future trends, you can try machine learning algorithms. This is known as supervised learning because you have the data to train the machines. A fraud detection system is trained in the same way.
Pattern Discovery: Another way is to use the technique to discover patterns. In this scenario, you cannot access the parameters to make predictions. So you have to look for those hidden patterns that can help you make a meaningful prediction. And this is known as the unattended model because you don’t have predefined labels. Clustering is the most popular algorithm for this.
Suppose you work with a telephone company and there is a need for a network of towers in an area. In this case, the clustering technique is the right one to decide on the tower locations. This ensures that the users in the area get the best signal strength.
In short, this was an introduction to data science and the technology it uses in various areas. Hopefully, the information will help you get a much better idea of what the term refers to and how you can take advantage of it.