In<\/em> the U.S., data scientists<\/strong> are experiencing some of the best job choices, based on the average salary and the number of available opportunities in the field. With the emergence of big data, businesses have explored a new asset \u2013 data that can help them experience huge growth when leveraged properly. You may already know that almost all the giant tech companies including Google, Facebook and Amazon have leveraged data in order to create their own business platforms. This perfectly explains the need for a different set of trained professionals who can organize, analyze, and derive valuable insights from data \u2013 the data scientists<\/strong>.<\/p>\n

1- An overview of the skills needed to become a data scientist<\/em><\/strong><\/h3>\n

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Typically<\/em>, data scientists<\/strong> have a solid understanding of software development, database systems, predictive analysis, and statistics. This makes the role somewhat different that needs the skills of both a statistician and a computer scientist and this is the main reason why data scientists<\/strong> are experiencing high demand.<\/p>\n

At its simplest form, data science revolves around the collection, storage, and analysis of huge amounts of data. In the entire process, data scientists<\/strong> make use of a lot of advanced tools and technologies that help them to a good extent in performing their activities. For example, advanced solutions like artificial intelligence, machine learning, powerful analytics tools etc enable them to process and understand huge volumes of data at unprecedented speeds. Data scientists<\/strong> are also the person responsible for translating the insights for other people in the organization, including stakeholders and senior executive \u2013 decision-makers in short. For example, they may decide on the form of data that is needed to be filtered into a storage system or pass details about consumer behavior on to other departments for building more successful and targeted campaigns.<\/p>\n

Now consider the fast pace at which more-advanced and diverse tools and technologies in the field of data science are emerging. What we can learn from all these is how different this industry will be in the coming future. Probably you are already aware of the fact that the job of data scientists<\/strong> has already been considered as the 21^{st<\/sup> century\u2019s sexiest job. And these days, job analysts across the globe are strengthening the statement as well. But how are they predicting this? What are the probable reasons? Let\u2019s explore.<\/p>\n}

2- Why the future of data scientist is extremely bright<\/em><\/strong><\/h3>\n

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Most<\/em> of us have gone through some articles portraying that the field of data science is already saturated. While it\u2019s a fact that there is a huge number of data scientists<\/strong> are working in the field and a lot of aspirants are waiting to join the league, but that isn\u2019t going to impact the promising future of data scientists<\/strong> anyway. Despite all those noises, there are no real reasons to believe that there\u2019ll be a paucity of jobs for skilled data scientists<\/strong>. In fact, the very arguments utilized to form those statements are actually the reasons not to worry at all. Let\u2019s have a look at the reasons for which the future of data scientists<\/strong> seems to be bright.<\/p>\n

2.1- An exponential growth of data volume<\/em><\/h4>\n

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A<\/em> huge amount of data is being generated by both businesses and common people on a regular basis. A recent study reveals that the number of consumers that interact with data daily will be a whopping 6 billion by 2025. In addition, in 2018, the amount of total data in the world was 33 zettabytes and now this is projected to become 133 zettabytes by 2025. As the world is becoming more and more connected than ever through the increasing use of connected devices, data generation will keep on rising. And data scientists<\/strong> will be central in helping businesses leverage that data effectively.<\/p>\n

2.2- Increased commoditization<\/em><\/h4>\n

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It\u2019s<\/em> evident now that a significant number of tasks performed by data scientists<\/strong> is getting commoditized increasingly \u2013 a huge number of machine learning frameworks now come with libraries that contain off-the-shelf models which are pre-trained, pre-architectured, and pre-tuned. The resulting effect is that a well-rounded data scientist<\/strong> is now able to solve in a much shorter timeframe what an entire time wasn\u2019t able to solve in several months a decade ago. It means that hiring a well-rounded data scientist<\/strong> has become viable for a significant number of domains for which the idea was too complex or too expensive before. Tools and technologies will keep on appearing and disappearing, but they\u2019ll be targeted at increasing the productivity of data scientists<\/strong> and thus, their net value to a business.<\/p>\n

2.3- The field is still evolving<\/em><\/h4>\n

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It\u2019s<\/em> a fact that any field without growth potential becomes stagnant at some point in time. It also indicates that the jobs within those fields need to change in order to stay relevant, but that isn\u2019t the case with the data scientist<\/strong> job. Since there is no sign of slowing down with a significant number of opportunities gearing up to appear, probably it\u2019s the best time for people looking to become data scientists<\/strong> to start preparing. Of course, there\u2019ll be some probable minor changes like someone working in the position of a data scientist<\/strong> in an organization may not be doing the same thing at another company. In a way, it\u2019ll be helpful for aspiring data scientists<\/strong> as they\u2019ll be able to focus on learning more specialized skills and do what\u2019s most meaningful to them.<\/p>\n

2.4- The emergence of data privacy regulations<\/em><\/h4>\n

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You<\/em> may already know that in the European Union, the GDPR (General Data Protection Regulation) took effect in May 2018 for countries. This implementation increased the need for data scientists<\/strong> to the organizations because of the need for storing data responsibly. One major aspect of the GDPR is that it allows consumers to ask the companies to delete some sorts of data. These days, people have become increasingly conscious about their online privacy and security and thus, they consider different aspects of giving away their personal information before actually doing it. They now understand what can be the probable consequences of the occurrence of a data breach. As a result, it has become impossible for companies to handle customer data irresponsibly. In addition, the GDPR is probably just the beginning with some more privacy rules pertaining to consumer data waiting to be implemented. In this scenario, data scientists<\/strong> are the best people who can guide the businesses on adhering to those regulations while leveraging the power of that data.<\/p>\n

2.5- The task of leveraging the power of data is complex<\/em><\/h4>\n

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Businesses<\/em> may have the opportunities to capture a massive volume of data regarding website interactions, customer transactions etc from different sources. But what if they aren\u2019t in a position to store, analyze, and derive insights from that data? Simply, the data is of no use. And that\u2019s exactly where data scientists<\/strong> come into the picture. Equipped with huge skillsets, these trained professionals only can help the businesses to get a competitive edge and accomplish their business goals. As we\u2019ve already discussed that the increasing use of high-end devices will result in a more connected world where more amount of data will be generated on a regular basis, the scenario will become even more complex without the help of data scientist<\/strong>s. And for data scientist<\/strong>s, it\u2019ll be something like an ongoing opportunity.<\/p>\n

3- Closing thoughts that every aspiring data scientist should consider<\/em><\/strong><\/h3>\n

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While<\/em> the above points demonstrate the key factors that will be instrumental in making the future for data scientists<\/strong> bright, aspiring data scientists<\/strong> also need to focus on some crucial things. First of all, there\u2019s no denying that now there is a steady supply of average data scientists<\/strong> to the industry who can surely perform at a good level but may not be able to reach the exceptional mark. And to become a top-notch data scientist<\/strong>, you\u2019ve to prepare yourself through the best way possible. Second, the industry will become competitive at some point of time in future, so it\u2019s better to start planning now to rise above the competition.<\/p>\n

Assuming you\u2019ve your fundamentals right, you need to decide on the avenue you\u2019ll be taking to become a good data scientist<\/strong> wisely. There\u2019re a significant number of options available out there like self-learning, traditional way, bootcamps etc. All of them come with their own pros and cons. However, there\u2019re some factors that differentiate a bootcamp from other avenues. For instance, if you\u2019re ready to become a data scientist<\/strong> the hard way, joining such a program would be your best bet. There, you\u2019ll be able to learn the concepts, tools, and technologies that are only applicable to real-life business issues and that too in a much shorter amount of time and from professionals working in the industry. In addition, most of the bootcamps offer job assistance after successful completion of the programs, so stepping into the professional field shouldn\u2019t be an issue. But again, if you notice, we\u2019ve mentioned the term \u2018hard way\u2019 because, from a data science bootcamp, you\u2019ll only get what you\u2019ll be putting in during the program in terms of time, effort, and diligence.<\/p>\n