Making a career change, no matter how big or small is never an easy feat. However, when you’re moving to a field that requires as much technical knowledge as data science, things can get exponentially more difficult. That’s why proper preparation is essential.
But here’s the thing: no matter how well-prepared we are, there will always be obstacles on our path to success. Whether internal or external, these hindrances can make it difficult for us to achieve our goals. And sometimes, they can even deter us from following our dreams. Fortunately, however, the better our understanding of the common pitfalls we’re likely to encounter, the better we can prepare for them.
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So, if you’re thinking about transitioning to a data science career (especially if you’re coming from a completely different background), these are the top traps you’ll want to avoid.
Pitfall 1 – Not Knowing What to Expect
One of the biggest mistakes you can make going into a data science career is to do so without sufficient research. Yes, it’s a well-paid job, it’s in high demand, and it’s becoming increasingly sought-after. But do you know what working in data science actually entails?
Contrary to popular belief, data science is not just about creating stellar algorithms. Yes, you will have to polish your programming skills (more on which later). But much more importantly, you’ll have to develop your analytical thinking so that you can actually do great things with the data you collect.
So, if you’re thinking about getting started with data science, you must be well aware of your main task: using correct information to make the best possible decisions and predictions that won’t fall flat when applied in the real world.
Pitfall 2 – Waiting for the Perfect Time
One of the biggest mistakes people make when transitioning to a career path different from their current one (or getting started with their job-seeking) is that they think they have to be the perfect candidate to apply for a job.
Instead of putting the transition off, start taking proactive steps towards your new career path. According to Skillcrush, the best way to become a data scientist is to learn languages and frameworks like Python, R, SQL, Hive, Pig, MongoDB, Cassandra, HBase, Hadoop, Spark, etc.
Don’t have any of these skills? That’s perfectly OK. Start small and start applying to jobs or projects. The more experience you have, the more confident you’ll feel in going after your goals. It’s that simple.
Pitfall 3 – Padding Your Resume
Here’s a thing most people do to some degree: they pad their resumes thinking that it will get them further than work. But that’s just a liability waiting to happen.
Instead of trying to make yourself look like the perfect candidate on paper, put your efforts into describing your skills and experiences as best as you can. Not only are they more likely to come off as appealing to employers, but they could also make the difference between you and someone with a similar skill set.
Moreover, if you’re feeling unconfident about your practical knowledge, try to keep in mind that employees sometimes prefer to hire beginners than experienced professionals. Especially when looking to build a team that will grow together with their business.
Pitfall 4 – Disregarding Soft Skills
If you know why you should never lie about your competencies, you probably also understand why you need to consistently work on all your skills, including soft skills.
As a future data scientist, you won’t just be sitting in front of a computer, creating algorithms.
You’ll be working with different members of your team (some of them without any technical knowledge whatsoever) on finding the best solutions to the presented problem. And to do that successfully, you need to be a great communicator. You also have to think critically, actively listen, be self-motivated, and have a high level of curiosity for the industry you’re working in.
Pitfall 5 – Not Doing Industry-Specific Research
When transitioning to a career in data science, it’s important to know that each industry is going to have its own challenges. For example, if you’re looking to enter the healthcare industry, expect the nature of your work to be entirely different than in the ecommerce sector.
With this in mind, you should structure your transition in two ways.
First and foremost, determine if there’s a specific field you’re looking to enter. For example, someone with a background in finance could be in the perfect position to become a data scientist with a bank or investment firm. But if you’re all about innovation, your personality may be more suited to the tech industry.
Secondly, be prepared to diversify your skills based on the industry you’re entering. If you expect to be working with a mixed team, you’ll have to know how to effectively visualize the data you collect so that it resonates with the people you’re collecting it for. But, without being skilled in the right software, that’s going to be relatively hard to do.
Pitfall 6 – Thinking You’re Not Cut out for It
Last but not least, let’s address one of the biggest myths about data science: that you have to have a degree or even a Ph.D. to enter the field.
Sure, a degree in mathematics, statistics, or computer science can be helpful if you’re thinking about a transition to this type of career. Especially if you’re interested in research instead of real-life application. But is it necessary? Not in the least. In fact, a background in a non-technical field could give you an edge over a lot of your future competition.
But, essentially, what matters the most is your preparedness to do hard work, your experience, and your capacity to grow together with the demands of your job. If you’ve got these, then you’re good to go!
Getting Started with Your Career Path
There you have it, the top pitfalls you want to avoid when transitioning to a data science career track. As you can see, this is a field open to anyone prepared to do some serious work.
Will it be easy, becoming a successful data scientist? Heck no. But, if it’s what you truly want, you’ll find that it’s one of the most satisfying job experiences you can give yourself. (And the fact that it pays well and offers numerous opportunities won’t hurt either.)
This article is republished from hackernoon.com.
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