You probably already heard about data science. But what is it exactly?
Data science is a mix of different algorithms, tools, and machine learning principles whose goal is to discover hidden patterns in raw data. In case you are wondering if this isn’t what statisticians do, the truth is that there is a main difference between the two – while ones explain, the others predict.
What Does A Data Analyst Do?
As we just mentioned above, a data analyst tries to predict the future, what will happen, based on the patterns they discover in raw data. It’s important to keep in mind that a data scientist doesn’t only do exploratory analysis to get new insights as he also uses different machine learning algorithms to identify the occurrence of a specific event in the future.
We can state that a data scientist looks at data from many different angles and perspectives, some of which may have not been known before.
Simply put, data science is mainly used to make predictions and decisions. To do so, a data scientist needs to use:
Predictive Causal Analytics:
This is the method you need to use when you want to predict the possibilities of a specific event in the future. For example, imagine that you are giving a credit to your customers. You should use predictive causal analytics to determine the likelihood or the probability of customers making their credit payments on time. In this case, your model should be built around the ability of a customer to pay you back. This means that you would ed to study the customer payment history.
This is a new field within data science that is becoming more and more popular. After all, you should use prescriptive analytics when you want a model that can make decisions on your own. As you can easily imagine, this model needs to have dynamic parameters that you can easily adjust when needed.
As you can understand, prescriptive analytics isn’t only about predicting but it also involves the suggestion of actions. A simple example of this is self-driving cars. After all, a self-driving car keeps collecting data that is then run on algorithms to ensure that the car can make its own decisions such as when to speed up, when to slow down, which path to take, and even when to turn.
Machine Learning For Making Predictions:
Machine learning can be used for a wide range of industries. For example, if you have transactional data from a finance company, you can build a model to predict the future trend using machine learning.
It’s important to keep in mind that this involves supervised learning since you are training your machines based on the data that you already have.
Machine Learning For Pattern Discovery:
When you want to make predictions but you don’t have any parameters, then you need to look at the dataset and find the hidden patterns. While there are many different algorithms you can use, the most common one is Clustering.
Why Data Science?
Many different reasons are making data science more popular every day. The main ones include:
Data Used To Be Small And Structured:
Companies are using more and more data every day. You probably already heard the term big data. Besides, the data comes from a wide range of places which means that it is no longer structured as it was a few years back. While before small and structured data could be analyzed simply by using BI tools, this is not the reality anymore.
Better Understanding Of Customers:
Customers are the center of businesses. Without customers, businesses can simply close doors. So, the more they know their customers, the better they will be able to serve them. So, by looking at customer’s existing data such as age, income, purchase history, and browsing purchase, you can now train models more effectively. This means that you can recommend products to your customers more effectively, with more precision.
Use Data On Your Favor:
Above, we referred to the self-driving car example. Ultimately, what we expect is that a car can take you everywhere and arrive safely at the location. As we mentioned, self-driving cars keep collecting data from numerous sources such as cameras, radars, sensors, among others. Then, based on this data, the car will know when it can speed up or needs to slow down, when to take a turn, among others. This is done with the aids of advanced machine learning algorithms.