Data Science is a domain that has been offering tremendous job opportunities lately. This domain is booming with job opportunities for qualified professionals. The job sector in the Data Science field is only going upwards, and this trend is here to stay for quite a long time now. According to the U.S. Bureau of Labor Statistics predicted that by 2026, this sector will see 28% of growth in job opportunities. This figure translates to nearly 11.5 million jobs in the domain.
But there is also one more stat that is on a negative trend. It is a job vacancy left unfulfilled. Sources and industry experts point out the lack of qualified professionals as the main reason behind positions remaining vacant. In Indian alone, 93,000 jobs were vacant at the end of August 2020. And 70% of those vacancies are for positions in Data Science with less than 5 years of experience. If we go global, there is a cumulative stat from CIO, Burtch Works, Harnham, McKinsey, Computer Weekly, and Women which claims that there is a shortage of 250,000 in the year 2020 alone. And during the covid-hit period, these figures have shot up and since then have been up and down.
Taking MOOCs
Hopefully, the biggest advantage the internet offers to its billions of users is its ease of access to information on anything. Data Science, like any other domain, has been immensely helped and profited from the internet. It helped millions of professionals to connect and learn Data Science through thousands of online courses that teach these beginners the important lessons of Data Science.
The major course which we are talking about is the MOOCs or Massive Open Online Courses. Top scientists and universities offer these MOOCs on Data Science that anyone can take up and master. If you are looking to upskill or make a career transition to Data Science, this is probably the best way to learn Data Science. Some offer the courses for free, which covers fundamental materials, and others charge for their exclusive training that comes with a certificate.
Talking about the pros of MOOCs, like Data Science Certification, are often taught by industry experts belonging from industry and academia. They serve the purpose of setting the tone and help in getting started with little investment. Speaking about the cons of it, well, there are plenty of courses to choose from which makes choosing the best one a difficult task, little individual attention, missing the human element, content may sometimes be more theoretical than practical, very less personal mentoring and guidance won’t fetch the desired results.
By reading books and online resources
Another method by which you can learn Data Science from scratch is by reading books and other online resources. There are plenty of resources that you can find on the internet as well as books. To get started in Data Science, you can refer to these books for Mathematics, programming, and statistics before going for a paid training or internships that we will discuss later. Let’s gauge the effectiveness of these books:
Talking about the pros of books, you have many books to choose from catering to specific needs for learning Data Science for Beginners. Easy access through many books and online mediums, as e-Books and audio forms. It will stay with you and can be accessed anytime without any restrictions. Coming to the cons of reading books, it might be boring as unnecessary points creep in which you might not need, is time-consuming, difficult to understand sometimes because of an absence of human factor, etc. Take a look at the following video on Data Science Tutorial from industry experts to learn Data Science step-by-step.
Gaining practical experience through online competitions
Domains like Data Science couldn’t be mastered through books or MOOCs alone. They need practical exposure to comprehend better. For that, you must participate in Hackathons and competitions on platforms like Kaggle, Driven Data, Machine Hack, and many more. These platforms offer plenty of resources in the form of challenges to learn from and gauge your understanding by working on projects like open-source ones. You participate, comment, get feedback by showcasing your works, and hence you grow with the help of a like-minded community.
Talking about the pros of this method, there are lots of competitions and challenges that you can participate in, and learn from. Will be able to start networking with professionals from around the world, instill a community-based approach, etc. Coming to the cons area, it can get frustrating sometimes, many of these challenges are tough to crack thus can be demoralizing, datasets presented are clean and transformed which you have to make in a real-life scenario and will not receive such clean datasets to work upon, couldn’t replace the real working conditions.
Gaining work experience through internships
We come to the next and last method, that is through Internships. It is the closest that you can get to real-life scenarios. You have to work as a permanent employee and complete their deadlines, but you get to see a Data Science professional from up close. And you can use this opportunity to understand the working datasets, the algorithms used, and how to tackle the stressful situation in a workplace and other etiquettes. But to get internships can be tough to seize as they require prerequisites that you must meet, like the skill sets that you must-have for the Data Science role. If you are aiming to crack an interview, check out Data Science Interview Questions
Talking about the pros of internships, it’s the closest to working conditions and helps in smoothening your transition to a full-time career. Speaking of cons of internships, well, unless you are very clear and passionate about the career you’re focused on this method won’t be that helpful, internships don’t guarantee you a job at the end of it, etc.
Conclusion
Get ready with appropriate skill sets to grab any opportunity that presents itself and start networking with people and professionals. Share your doubts and queries with an expert or a mentor in a training platform. They may better guide you with phases and opportunities that you may not be aware of.
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