It's no big surprise that data science is one of the most exciting careers out there today. Glassdoor's annual survey has consistently ranked "data scientist" as the number one job role in the United States in the last two years. Not only is data science regarded as a highly rewarding and challenging career, but there's also a huge dearth of data scientists today. In fact, it's expected that there will be 2.7 million data science job vacancies by 2020.
Despite the fact that data science is very much in demand, there is still a lot of confusion that surrounds the field as a whole. Most people look at data science as an esoteric field that only math, statistics, or computer geniuses can get into. In this article, we talk about some of the top myths surrounding data science and why they're simply not true.
1. Only brilliant mathematicians can become data scientists
This is completely untrue! Of course, data science does use some fundamental mathematical concepts like regression and some probability theorems. Also, people with a solid foundation in math tend to grasp some of the deep learning algorithms a lot more easily.
Yet, you don't have to be a math genius to become a data scientist. A love for data, finding patterns, and doing rigorous analysis; yes. The ability to prove every theorem in the world; a big no.
2. You have to be an excellent programmer to be a good data scientist
If not having any coding skills is the only thing holding you back from becoming a data scientist, rest assured. There are plenty of data scientists who get away with practically no coding skills. Having said that, you will probably have to pick up some programming skills along the way because they will help you in your data science career. Yet, a beginner's course in Python or R is usually enough to get you going. When it comes to data science, your ability to organize data, identify patterns, and recommend solutions is far more important than your ability to write code.
3. Data Science is all about the tools
Most outsiders tend to believe that all you need to do to become a data scientist is master tools like Python, SAS, and R. While these tools are undoubtedly important, mastering the tools themselves is simply not enough. Data science is all about knowing which tool works best for which business problem. By using a particular tool in the wrong context, you can even end up corrupting the data. Think of data science tools like an electrician's tool-kit; every electrician chooses which tools to use based on their experience and the situation at hand.
4. Data Scientist = Business Analyst
Data science is a term that's only become commonplace in the last 10 years. Business analysts, on the other hand, have been around for decades. Business analysts are usually experts in a particular domain. They understand business problems, analyze data to generate insights, and then make recommendations to business teams. Data scientists, on the other hand, are not domain experts. They are simply data experts. Unlike business analysts who analyze overall data on growth, profits, etc. data scientists look at capturing specific data to identify patterns and trends.
5. You must have a degree in data science to become a data scientist
When it comes to data science, having a graduate degree in the field is neither necessary nor sufficient. There are plenty of people who've transitioned into data science from fields like marketing, learning and development, and lower tier finance jobs. While the lack of a data science degree can make the initial learning curve slightly steeper, it's definitely possible to master the data science tools and techniques. Even a short course, such as Xccelerate's 6 week data science program, can set you on the right path. If you want to be entry level job ready, we recommend the immersive data science & machine learning course in hong kong.
On the other hand, a degree in data science is no guarantee of a job in the field unless you have a mastery over the tools and approaches. Even after you get a data science job, it can take months for you to master real-world data science skills.
6. As a data scientist, you have a career for life
It wouldn't be completely accurate to say that data science is a lifetime career. While it's true that data science offers a variety of interesting job roles and an exciting career trajectory, it is likely to evolve. Many tech roles that were in vogue ten years earlier have now all but disappeared thanks to the advances in technology. However, the good news is that working in data science will keep you well-versed with the latest technological advancements. As long as you don't bury your head in the sand and are ready to learn new skills, you will be on a great career path.
7. Building the models is hard, getting actionable data is simple
Most outsiders believe that data science is all about building predictive models; that's part of the allure. Yet, as most data scientists will tell you a large part of their day is actually spent cleaning, sorting and organizing data so that it's actionable. In other words, if the data is not standardized and sorted, even the most powerful predictive model won't work.
Final Thoughts
We hope this article helped you understand the reality behind some popular myths that surround data science. If you love business problems and believe in solving them through data, then data science may be the right career choice for you. Don't fall for the intimidating narrative that surrounds the field and just explore it to see if it works for you.
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