The<\/em> data scientist is considered as the best job in the USA for 2019 with $108,000 as a median base salary, plus an attractive number of predicted openings. According to the prediction made by the IDC (International Data Corporation), in 2020, worldwide revenues for business analytics and big data will reach over $210bn. These statistics are more than enough to motivate someone to learn data science<\/strong>. If you need some more reasons to learn data science<\/strong>, it\u2019s associated with a huge number of new applications and industries that emerge from the judicious use of massive amounts of data. From self-driving cars and robots, bioinformatics, speech recognition to object recognition, neuroscience, and many more \u2013 data science is central to the entire organization dealing with any of these.<\/p>\n

But the question is: despite all these attractive career options and the whopping number of people looking to learn data science<\/strong>, why there is still a huge talent gap? Probably the answer is aspiring data scientists overlooking some simple yet fundamental aspects when trying to learn data science<\/strong>. Unquestionably, in the data science field, much comes down to the core skills like mathematics, statistics, coding, and so on. But those skills alone don\u2019t cut it. In order to learn data science<\/strong> effectively, you\u2019ve to take care of some key aspects as well.<\/p>\n

Here, we\u2019ll be discussing the key aspects you must take care of when you\u2019re planning to learn data science<\/strong> to increase your chances in getting your dream job.<\/p>\n

Useful advice to keep in mind when you\u2019re planning to learn data science<\/em><\/strong><\/h3>\n

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We<\/em> hope that by being able to understand the following aspects, you\u2019ll be able to sail through them efficiently and make your path toward a data science career a little bit easier. Let\u2019s dive in.<\/p>\n

1- Make sure you truly want to learn data science<\/strong><\/em><\/h4>\n

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Undoubtedly<\/em>, data science is one of the most promising career options available right now. The salaries are excellent, you get to work on interesting things, and overall a covetous job to a lot of people. Unfortunately, even if you\u2019re passionate about stepping into the field and want to learn data science<\/strong> desperately, you may not be in love with the process. Put simply, if you don\u2019t like the day-to-day tasks that are integral parts of data science professional\u2019s job, it\u2019ll be extremely difficult to maintain the motivation. It\u2019s important to understand that for most people trying to learn data science<\/strong>, it sometimes takes months before they get to work on full-scale data science projects. And after spending a couple of months in your effort to learn data science<\/strong> if you realize that you don\u2019t like the process, it can be a huge personal blow. While data science is an excellent profession, there\u2019s definitely a significant amount of frustrations which come with it. So, do your research before you start to learn data science<\/strong>. Try to learn about the tools and tasks a data science professional does, before you actually decide to build a career in data science.<\/p>\n

2- Select the appropriate role<\/em><\/h4>\n

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This<\/em> is another key aspect that has to be considered before you start to learn data science<\/strong>. In the data science industry, different types of roles are available \u2013 from a data scientist and a machine learning expert to a data engineer to a data visualization expert, and many more. Depending on your preferences, educational background, and work experience, aiming at one role would be relatively easier than another one. For instance, if you\u2019ve work experience as a software developer, it wouldn\u2019t be much difficult for you to become a data engineer. So, before you start to learn data science<\/strong>, it\u2019s important to be clear about what role you want to get into in order to be able to shortlist the skills to hone. If you\u2019re not clear about the differences between different roles, there\u2019re ways to find them out. These include talking to people working in the industry, seeking mentorship from people, choosing the role which suits your field of study etc. However, it\u2019s important not to just blindly jump on to a role. First you should clearly understand what it requires and then prepare for it.<\/p>\n

3- Where to learn data science<\/strong><\/em><\/h4>\n

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Now<\/em> that you\u2019ve decided on the role, the next step is deciding on where you should learn the required skills from. Because of the huge demand for data science, there\u2019re lots of avenues available these days, from which you\u2019d need to take your pick. Finding material to learn data science<\/strong> isn\u2019t difficult but from where you learn can make a huge difference. Let\u2019s have a look at the most popular avenues so that you can make an informed decision.<\/p>\n

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• MOOC: With more students enrolling to boost their knowledge, MOOCs (Massive Open Online Courses) are experiencing huge popularity. They offer self-paced courses, usually free to avail, in a huge list of subjects and topics. If you want to learn data science<\/strong> through a MOOC, you can dedicate a couple of hours daily and advance whenever you can.<\/li>\n
• Books: This old school method can be your best resource to learn data science<\/strong> if you pick wisely. There\u2019re lots or resources available in textbook format from where you\u2019d need to take your pick in accordance with your requirements.<\/li>\n<\/ul>\n

  Despite all the advantages, self-learning methods like the above ones aren\u2019t the best for everyone. Here, it\u2019s quite easy to get demotivated as it\u2019s actually difficult to learn data science<\/strong>. It\u2019ll take a lot of energy, a lot of work, and a huge amount of time from you. If you want to gain real data science knowledge, learning in a true-to-life data environment is immensely crucial. Let\u2019s check out another avenue to learn data science<\/strong>.<\/p>\n

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  • Bootcamp: Bootcamps are the most useful and popular avenue when it comes to learn data science<\/strong>. They offer several professionally designed modules that help you learn almost everything you\u2019d need to become a successful data science professional. Some people may question the depth with which their topics are covered, but the truth is they excel at offering a good introduction and standard level of expertise in all data-related subjects. More advanced levels are introduced when you complete the initial levels, so you shouldn\u2019t be worried. However, before enrolling with a bootcamp to learn data science<\/strong>, you need to make sure that the program curriculum is professionally effective, the bootcamp has a proven track record, it offers job assistance after successful completion of the program.<\/li>\n<\/ul>\n

    4- Practical applications are absolutely important<\/em><\/h4>\n

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    Regardless<\/em> of the avenue you choose to learn data science<\/strong>, you should always focus on practical applications instead of theory only. While undergoing training and courses, it\u2019s essential to try to explore the real-life applications of the things you\u2019re learning. This would not only help you understand the concepts but also help you get a deeper sense of how those would be applied on real-life. Just be prepared to follow these things meticulously.<\/p>\n

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    • Make sure you do every assignment and exercise to understand the applications.<\/li>\n
    • If you get stuck somewhere, consider looking at the solutions by people who\u2019ve worked in the field. It\u2019d help you understand the right approach.<\/li>\n
    • Try to apply your learning on a couple of open datasets. Even if you don\u2019t understand the core concepts initially, you\u2019d get an idea of the assumptions that would help you greatly at the later stages.<\/li>\n<\/ul>\n

      5- Join a community<\/em><\/h4>\n

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      Now<\/em> that you\u2019ve started to learn data science<\/strong>, it\u2019s important to join a community of aspiring data science professionals. Taking up something new, especially a difficult field like data science, often feel like a bit uphill task when you do it alone, but when you\u2019ve like-minded people alongside you, the task becomes a bit easier. Either you can have a group of aspiring data science professionals you can interact with physically, or you can have an online group that has people who are planning\/have started to learn data science<\/strong>.<\/p>\n

      6- Work on to develop soft skills<\/em><\/h4>\n

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      Though<\/em> some people think that soft skills aren\u2019t that much important for someone stepping into a highly technical field like data science, in reality, these skills are equally important like hard skills. Soft skills that most of the employers like to see in the applicants include leadership, time management, communication, and collaboration. Soft skills will become highly important when you\u2019ll be working in the field, and thus, it\u2019s important that you focus on improving it at the same time when you start to learn data science<\/strong>.<\/p>\n

      Conclusion<\/em><\/strong><\/h3>\n

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      Finally,<\/em> you should understand that a good professional is learning always and it has become even more important in today\u2019s dynamic world. Regardless of the avenue you choose to learn data science<\/strong> or the position you\u2019ll be working at, you\u2019ve to keep on learning in order to stay up to date and to grow as a professional. Learning is quite a personal process and there isn\u2019t something that equally works for everyone. Just try the above suggestions out and you\u2019ll surely be able to find out the way to learn data science<\/strong> suited best for you.<\/p>\n