Data science is not about making complicated models. It is not about a cool visualization. It's not about writing code. Data science is about using data to create the greatest possible impact for your company. Now the effect can be in the form of multiple things. It can be in the form of visions. It can be in the form of data products or it can be in the form of product recommendations for your company. Now to do these things, you need tools like complex forms, data visualizations, or code writing. But basically as a data scientist, your job is to solve the real problems your company faces. What kind of tools do you use? No one cares. There are a lot of misconceptions about data science, especially if you go to YouTube. The reason for this is that there is a major imbalance between what is common to talk about and what is needed in this industry. From the perspective of the data scientist who is already working for a huge company, those companies really emphasize using data to improve their products.
History of data science
Before data science, we promoted the term data mining from an article published in 1996. This article refers to the comprehensive process of discovering useful information from data. In 2001, William Cleveland wanted to take data mining to another level. He did this by combining computer science with mining data. Basically, he made statistics much more technical than he thought would broaden the possibilities of data mining and create a powerful force for innovation. Now you can take advantage of the computing power of the statistics. This narration is called data science.
Meanwhile, this is also when Web 2.0 appeared as websites are no longer just a digital brochure, but a way to have a shared experience among millions and millions of users. These are websites like myspace in 2003, Facebook in 2004, and YouTube in 2005. We can now interact with these sites which means we can contribute, post comments, like, upload, and share that left our mark in the digital landscape we call the internet . And help create and craft the ecosystem that we now know and love today.
Big Data Appears
And imagine what? This is an enormous amount of data, and a lot of data, it has become very difficult to deal with by using traditional techniques. So, we called it Big Data. This opens up many possibilities for finding more ideas using data. But this also means that the simplest questions require only an advanced data infrastructure to support data processing. We need parallel computing technology like Map Reduction, Hadoop and Spark. So the emergence of big data in 2010 started to increase data science technologies to support business needs. The needs were about getting insights from large sets of unstructured data. Thus data science is described as almost anything related to data.