Data Science and Different Techniques

Data Science and Different Techniques

Data Science is a term that is getting quite popular these days. However, what does this mean and what type of skills do you need? In this article, we are going to answer these questions in addition to finding out some important information. Read on.

First of all, let’s find out what the term refers to. Basically, data science is a combination of many tools, machine learning techniques and algorithms. They are combined to find out hidden patterns based on the given raw data.

Primarily, data science is used for making important predictions and decisions through the use of machine learning, prescriptive analytics and casual analytics. Let’s get a deeper insight.

Predictive Casual Analytics: Basically, if you need a model that can predict the happening of a certain event down the road, you should use this approach. For instance, if you offer money on credit, you may be worried about getting your money back from the debtors. So, you can develop a model that can do predictive analysis to find out if they will be making payments on time.

Prescriptive Analysis: Also, if you need a model that has the ability to make decisions and modify them with dynamic parameters, we suggest that you do a prescriptive analysis. It is related to offering advice. So, it predicts as well as suggests a lot of prescribed actions and the related results.

If you want an example, you may consider the self-driving car by Google. The data collected by the vehicle is usable for training these cars further. Also, you can use many algorithms to add more intelligence to the system. As a result, your car can make important decisions, such as taking turns, taking the right paths and speeding up or slowing down.

Machine Learning: For making predictions, machine learning is another technique used in data science. If you have access to some type of transactional data and you need to develop a model to predict future trends, you can try machine learning algorithms. This is known as supervised learning as you have the data to train the machines. A fraud detection system is trained the same way.

Pattern Discovery: Another way is to use the technique for pattern discovery. In this scenario, you don’t have access to the parameters for making predictions. So, you have to look for those hidden patterns that can help you make a meaningful prediction. And this is known as the unsupervised model because you have no predefined labels. Clustering is the most popular algorithm for this purpose.

Suppose you work with a telephone company, and there is a need to start a network of towers in an area. In this case, the clustering technique is the right one to decide on the tower locations. This will ensure the users in the area will get the best signal strength.

In short, this was an introduction to data science and the technique it uses in different fields. Hopefully, the information will help you get a much better idea of what the term refers to, and how you can benefit from it.