Unquestionably<\/em> the role of data scientist<\/strong><\/em><\/a> has become one of the most coveted jobs in today\u2019s tech domain.<\/span> Be it professionals working in IT or other fields, or fresh graduates, this latest craze is taking the technology domain by storm. There has been a tremendous surge in the number of people who\u2019ve enrolled in one of the many schools that offer courses on and\/or related to data science.<\/p>\n

Leaderships are also gearing up to change their strategies based on their used\/unused data sources to get actionable insights from their present as well as future business models. Put simply, data-driven decisions are ruling the business world. Every company, regardless of how big or small it is, looking to employ data scientists<\/strong> who can understand analyze huge chunks of data.<\/p>\n

Now, the thing is that becoming a data scientist<\/strong> isn\u2019t easy at all, though some institutions try to portray it like that. You\u2019d need to invest lots of hard work, a significant amount of time and money. And if you want to go by the traditional way, simply it\u2019ll take a couple of years to become a data scientist<\/strong>. All these raise an obvious question \u2013 despite all these things, why are people putting their best effort in to become a data scientist<\/strong>?<\/p>\n

Here we\u2019ve put together the best reasons for which you should consider becoming a data scientist<\/strong>. Let\u2019s have a look at them.<\/p>\n

1- Key reasons to become a data scientist<\/em><\/strong><\/h3>\n

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You<\/em> may are already aware that the job of data scientist<\/strong> has been declared as the century\u2019s hottest job<\/span>.<\/a><\/em><\/span> Companies, both large and small, across the globe are clamoring to find these professionals who can work on data and then communicate their findings that can be beneficial to the companies. That\u2019s something your employer will be enjoying but what will you be getting as a data scientist<\/strong>? Let\u2019s find out.<\/p>\n

1.1- Developed analytical thinking<\/em><\/h4>\n

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As<\/em> a data scientist<\/strong>, you need to work with data.<\/span> Business decision makers heavily rely on people like you to understand the outcomes of their present and\/or future business models. As a result, your job responsibilities involve constant learning as well as practicing data science<\/em>.<\/p>\n

For example, you\u2019ll be able to understand all the crucial statistical bias types what are just strange things to most people. Therefore, you\u2019ll be more aware of real-life situations and will look at everything with an analytical bend of mind. So, your ability for analytical thinking will automatically become much higher than average.<\/p>\n

1.2- Unbeaten salaries<\/em><\/h4>\n

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In<\/em> the U.S., a data scientist<\/strong> with 1 to 3 years of experience makes an average of $106,000 per year and it\u2019s same in the European countries as well. Data scientists<\/strong> make 2-3 times more money than the local average salary.<\/span> We\u2019re not saying that money is everything, but it\u2019s always good to know that you don\u2019t need to worry about it, so you can concentrate on more exciting things.<\/p>\n

And if you become a data science manager, you can earn almost the same as the doctors do. So, without spending years to become a doctor, as a data scientist<\/strong>, you can earn almost the same and even more sometimes. Isn\u2019t that pretty amazing?<\/p>\n

1.3- Experience matters a lot<\/em><\/h4>\n

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Experience<\/em> is perhaps the most common word that can be seen any job advertisement and in general, companies want employees with a lot of it. However, when it comes to the experience of a data scientist<\/strong><\/em><\/a>, a couple of years of experience can easily help you climb the corporate ladder up, whereas in other fields it normally takes more than a decade. And it\u2019s a universal truth that wages match up with the experience levels.<\/p>\n

As of now, organizations across the globe are desperately looking for data scientists<\/strong>. This scenario may and probably will change in the future, but looking at the present formal education required to become a data scientist<\/strong>, it can be expected that this change will not take place any sooner than next 5-10 years. So, if you start your journey to become a data scientist<\/strong> now, after a couple of years, you\u2019ll be in a position that\u2019s in great demand.<\/p>\n

1.4- Job hunting becomes easier<\/em><\/h4>\n

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Getting<\/em> your first job often becomes a bit trickier, especially if you\u2019re aiming for a good job but this isn\u2019t the case with data scientists<\/strong>. These people are in extremely high demand and there\u2019s an acute paucity of them. Companies have recruiters dedicated solely to find these professionals.<\/p>\n

While other job applicants in other fields are pestering recruiters in different ways, as a data scientist<\/strong>, you don\u2019t need to even think about it. All you need to do is let the world know that you\u2019re looking for a job. The demand has become so dire that even if you\u2019re doing a job, hiring managers will try to lure you away with better offerings.<\/p>\n

1.5- A wide range of options<\/em><\/h4>\n

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Every<\/em> single day a huge amount of data is being generated by different sources. As a result, the data science field is evolving rapidly because of the increasing need for deriving actionable insights from that data. Data scientists<\/strong> come with a wide range of skillsets to leverage data to help businesses to make better business decisions. So, the work opportunities offered to them aren\u2019t only exciting but diverse in nature as well.<\/p>\n

There\u2019re many exciting fields have already emerged within the field of data science.<\/span> Some of them include machine learning<\/strong><\/em><\/a>, artificial intelligence<\/strong><\/em><\/a>, together with some newer technologies such as edge computing, blockchain<\/em><\/strong><\/a> etc that employ various techniques and practices within the data science field. So, as a data scientist<\/strong>, you\u2019re open to take your pick from a wide range of industries according to your preferences.<\/p>\n

1.6- Ability to start own entrepreneurial venture<\/em><\/h4>\n

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Creating<\/em> something of their own has been a dream of lots of people but they often fail to make it through because of their lack of expertise and knowledge. To become a data scientist<\/strong>, you\u2019ve to learn coding<\/strong><\/em><\/a>. And when you\u2019ve a solid understanding of coding, you\u2019ll be able to develop your own product(s), or the prototypes at least.<\/p>\n

It\u2019s a well-known fact that learning how to code opens up a new world for you. You may have heard the popular saying in the data science domain \u2013 a good coder may not be a good data scientist<\/strong>, but a good data scientist<\/strong> is surely a good coder.<\/span><\/p>\n

2- Key things you need to remember<\/em><\/strong><\/h3>\n

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Hopefully,<\/em> the above read has made you motivated enough to become a data scientist<\/strong>. Now, a common question asked by fresh data scientists<\/strong> is should I join a large or startup company? According to us, it entirely depends on your preferences and working style. Startups usually offer less micromanaging and more freedom. It also means that you\u2019ll receive less guidance and will need to figure out stuff on your own that can help in making progress.<\/p>\n

On the other hand, when you\u2019re employed in a big organization as a data scientist<\/strong>, you\u2019re likely to experience clearly defined pre-existing processes and more structured approaches. In general, you\u2019ll experience less freedom but will be able to be clear about job responsibilities.<\/p>\n

However, when you\u2019re a fresh data scientist<\/strong>, you shouldn\u2019t put too much stress in choosing one over the other. If you like an organization, you should give it a try regardless of its volume. If you aren\u2019t satisfied after a couple of months, you can try another.<\/p>\n

It\u2019s also important to note that you should be changing organizations after one or two years.<\/span> The key reason is the majority of the salary hikes you\u2019ll earn in your working life will happen in the first 10 years of your career. For example, you\u2019re hired by a company as a junior data scientist<\/strong> and work there for 2 years. Now, you\u2019re no longer a junior after 2 years and can earn a much higher salary as a data scientist<\/strong>, but it\u2019s unlikely that your company will give you that hike after 2 years. So, you should change the company at that point of time to earn the big bucks.<\/p>\n

Final takeaway<\/em><\/strong><\/h3>\n

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To<\/em> be able to enjoy all the above benefits, you shouldn\u2019t narrow your focus too much when you\u2019re learning to become a data scientist<\/strong>. You can get stuck in situations like you\u2019re an expert in a certain version of a particular technology but companies are actually looking for experts in another version. Ideally, you should try to obtain a broad understanding of data science fundamentals which will be far more valuable throughout your career as a data scientist<\/strong>. <\/em><\/a><\/p>\n

The best thing you can do is to spend the majority of your learning efforts on things that are timeless, such as the base technologies under advanced ones. And the most important part is you must not give up learning ever.<\/span> As we\u2019ve discussed that the field of data science is evolving, technologies and tools will come and go. It\u2019s your sole responsibility to obtain a good understanding of them to keep yourself on the same page with current industry trends to be able to be accepted by companies across industries.<\/p>\n