As long as there is ‘data’ in data scientist, Structured Query Language (or see-quel as we call it) will remain an important part of it. In this blog, let us explore data science and its relationship with SQL.
Is SQL needed to be a data scientist?
The short answer is yes. As long as there is ‘data’ in data scientist, Structured Query Language (or see-quel as we call it) will remain an important part of it. In this blog, let us explore data science and its relationship with SQL, including answers to the 5 Ws and 1 H – how, why, where, when, who and what. We will also learn the basics of database Management Systems (DBMS) and understand how being a data scientist could be the best choice for your career.
Data science is broad in its perspective and a data scientist requires deep knowledge of one or more of the various streams of mathematics, machine learning, computer science, statistical research, data processing and of course domain expertise. Each of these streams needs extensive work with data, be it collection, analysis or processing. If you are preparing for a data science interview, go through these essential data science interview questions.
Why is data science so popular?
The digital world is at its peak, and with growing demands and extensive marketing strategies data has become the key to all marketing purposes. For example, if I want to buy a new phone, I go to online shops like Amazon or Flipkart, browse through different brands, put a few in my cart, but decide to buy it later after some more research. Internally, the online shop would save my shopping cart and browsing history and show me suggestions for more phones when I come back later. Even if I don’t buy, the company would send me emails to remind me that my shopping cart is "still waiting for me." Data, thus plays the most important role in creating a relationship between buyer and seller. The more data customer shells out, the more customized a stream is presented to the buyer. This is not just true for e-commerce, but data science is proving extremely useful in many other domains like healthcare, manufacturing, banking, finance and transport.
Collection – Suppose you are at IKEA and checking out a fitted sheet. You buy the product and leave. Later you realize you want more of the same product and come back. You tell your friends how useful and inexpensive the product is and they are convinced to buy it too. Manufacturers use this data to understand the likes of customers and update their inventory to have more of the products that are popular. Further, constant feedback helps them bring improvements to their existing product.
Processing – The data from users is collected, and during the modelling and planning stage actionable insights are taken into account. For example, more customers looking for a particular colored fitted sheet or a specific cloth for their curtains.
Analysis – Imagine you bought a blue color fitted sheet, but realize for your room's ambiance that green would be a better choice, which is not available currently. Green is a common, popular color. With the analysis of human inputs and data management tools, it can be determined whether or not introducing a green fitted sheet would be a good idea, if it will serve the purpose of more customers, and bring more profit.
For demand forecasting and inventory management, we need to store all the user information including their purchases, likes and dislikes, feedback, etc. somewhere.
Yes, you got it – everything is stored in a database. SQL is thus vital for handling humongous amounts of data that need to be processed on a regular basis. It also acts as an important tool for the right marketing and feedback that data science intends to do. For example, if you don’t like a video that Facebook is suggesting you – you would say ‘hide this’ and Facebook will immediately ask you for a reason. These user preferences also need to be stored somewhere.
