In recent times topics like data science and data-analyst are what we see everywhere. We are shifting exceptionally to a data-centered environment in every aspect.
Data science is all about using data effectively to give as much impact as possible for your company. The impact can be in the form of multiple things, it can be in the form of insights, data products, or in the form of product recommendations for a company. Now to do these activities, you need tools like data visualizations or writing codes. The actual work of a data scientist is to solve the real problem of your company using data no matter what kind of tools you use. Data science is a combination of Computer science and Data mining.
We can now interact with all the websites, we can contribute post, comment, like, upload and share, leaving our footprint in the digital landscape we call the internet and, help create and shape the ecosystem we live and love today. That’s a lot of data, isn’t it? Now, these enormous amounts of data are too much to handle using traditional technologies. So we call this Big Data.
Big data opened a world of possibilities in finding insights using data, but it also meant that the simplest question requires sophisticated data infrastructure just to support the handling of the data. To do that we needed parallel computing technology like MapReduce, Spark, and Hadoop. So we can say that the rise of big data in 2010 helped raise the data science to assist the needs of businesses, to help access the massive unstructured data sets.
So the data science is something that has anything and everything to do with data. The most important part is its applications. Data science made it possible to train all sorts of machines with a data-driven approach rather than the knowledge-driven approach. Now, deep learning is no longer an academic concept, it is the class of machine learning that would affect our everyday lives. And, in this way AI and machine learning started dominating the world overshadowing every other aspect of data science, like all the skills we traditionally called business intelligence.
Now, generally, the data scientists are known as a researcher that is focussed on machine learning and AI, but Industry is hiring data scientists as Data analysts. Being a Data scientist is about how much impact you can have with your work. As a DS are not a data statistician, you are a problem solver. Basically, according to the company size, the work of a data scientist differs. For a start-up, there is a lack of resources so there is likely going to be only one DS, and she/he has to do everything, from setting up the whole data infrastructure to write some software code, to so analytical part, to build metrics by yourself and also to the A/B testing yourself. But this won’t be the case as the company sizes go big and big.