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Must have heard about big data, right? The time when big data entered our life, we got to know about the importance of data. We had an unlimited amount of data even before big data, but it was the way it was, and we had no idea what to do with it. We did not even have storage space for this enormous volume of data. When big data entered the market, everyone was worried about how to store the data, and every company started working on designing solutions for data storage. After some time and endless efforts, we were able to build Hadoop and other frameworks for storage.
After the solution of data storage was found, we shifted our focus towards how to use this data for our benefit. How can we use this data to make our business better? In those times, we were introduced to the term data science, which could help us getting leverage of that data. Data science is the combination of programming skills, mathematics, and statistics that help us uncover the insights of data. And then, we use those insights to make better decisions regarding business or any other thing. It lets us know about the behavior and trends of the data, which tells us about the market conditions. We can take the example of google the web search engine here. It keeps track of our activities and later on, it suggests things based on experience and the data it has about us.
The world we live in is data-driven, and data has been called the fuel of this century. There has been a surge in the popularity of data in the last decade due to social media, the internet of things, and smartphones. In today's world, the amount of data that comes in and goes out is unimaginable.
There used to be only structured due to its small size before the digital revolution. At that time, existing business intelligence tools were more than enough to take care of that data. However, when data started growing exponentially, things started getting bad for those tools, and it changes the complete equation. Now that we have a huge amount of data from multiple data sources like daily logs, social media, financial transactions, online portals, etc. The most amount of data we have is unstructured or semi-structured. With years passing, data will grow more and more and will be added to the already piled up data.
Let's look into the factors that indicate how data science and data scientists will become a need for every organization.
A company that deals with costumers is collecting a large amount of data through the transaction and other things, and most of them face a common challenge. That challenge is to analyze and utilize the collected data. It is the situation where a data scientist becomes their savior.
A career path is supposed to keep evolving otherwise, it can get stagnating. That evolution also brings new opportunities and career growth. Data science is a career that is broad and it keeps developing with new technologies. New tools and techniques are there every other days that bring new opportunities. That is a good thing for data scientists.
We are generating data every day, whether it be knowingly or unknowingly. As time passes, we will have to interact with data more and more. The world is generating even faster than the light of speed. As there will be data this huge, the demand for data scientists will increase accordingly.
We see how artificial intelligence is getting popular among businesses in today's world. In the concepts of deep learning and neural networking, we work with data, a lot of data. Like machine learning, that is very popular right now and is implemented in almost every other application. All the future concepts depend on data and to work with data, a data scientist is required. So, all in all, there is a very wide scope of data science in today's world.
There has always been a big debate about the data science dream team. Who and who should be there in the team, which role plays which part and many more questions like this. One thing is for sure data science is a team sport, and all the members need to perform their share responsibly. The main job of a data science team is to work through data and bend it in a way that every critical insight of that data is out, clear and shiny. That was everything about a data science team.
Let's now look at the other debate, do we need specialists in the team or generalists? There is more than one role that needs to be played in the team, and sometimes a single person performs multiple tasks. And for that, you need to be an expert in not just one skill. One can join any data science Bootcamp to be an expert in the field.
Well, if we look into what a generalist and a specialist mean. A generalist is someone who has expertise in some areas of data science but has sufficient knowledge about the complete cycle of data science. Whereas if we talk about a specialist, he is a person who is an expert in just one domain of data science. To be fair, having a generalist in the team is better because he can counter any emergency, as he knows about almost everything when it comes to data science.
If we talk about specific roles in a data science team, there are data engineers, data scientists, business analysts, machine learning engineers, and software developers. It is a combined effort of all of these members that make a data science project successful.
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