{"id":8404,"date":"2021-10-16T14:00:40","date_gmt":"2021-10-16T14:00:40","guid":{"rendered":"http:\/\/TheNextWeb=1370054"},"modified":"2021-10-16T14:00:40","modified_gmt":"2021-10-16T14:00:40","slug":"9-reasons-why-youll-never-become-a-data-scientist","status":"publish","type":"post","link":"https:\/\/www.londonchiropracter.com\/?p=8404","title":{"rendered":"9 reasons why you\u2019ll never become a Data Scientist"},"content":{"rendered":"\n

Disclaimer: This story is not meant to discourage you. Rather, it should serve as a long hard look in the mirror.<\/em><\/p>\n

So you\u2019re enthusiastic about Data Science, you\u2019ve read a couple dozen blog posts and completed a few online classes. Now you\u2019re dreaming of making this your career. After all, it\u2019s the sexiest job of the 21st century, according to Harvard Business Review<\/a>.<\/p>\n

But despite your enthusiasm, Data Science might not be for you. At this moment in time, you\u2019re holding too many illusions and false stereotypes.<\/p>\n

Now, your task is simple: Remove the things that hold you back! And you\u2019ll be surprised at how fast you move forward.<\/p>\n

1. You think your degree is enough<\/h2>\n

You have a master\u2019s degree in a quantitative field, or maybe even a Ph.D. Now you want a head start in Data Science.<\/p>\n

But have you ever used a shell before? Have you felt the intimidation that can come from command-line interfaces when you stumble upon errors? Have you ever worked with big databases \u2014 on the scale of Terabytes?<\/p>\n

If you answer one of these questions with no, you\u2019re not ready yet. You need some real-world experience and build some real projects. Only then will you encounter the type of problems that you\u2019ll face every day as a Data Scientist. And only then will you develop the skills to solve them.<\/p>\n

Congratulations on your degree. Now get cracking on the hard work.<\/p>\n

2. You lack passion<\/h2>\n

Have you ever invested an entire weekend in a geeky project? Have you ever spent your nights browsing GitHub while your friends were out to party? Have you ever said no to doing your favorite hobby because you\u2019d rather code?<\/p>\n

If you could answer none of the above with yes, you\u2019re not passionate enough. Data Science is about facing really hard problems and sticking at them until you found a solution. If you\u2019re not passionate enough, you\u2019ll shy away at the sight of the first difficulty.<\/p>\n

Think about what attracts you to becoming a Data Scientist. Is it the glamorous job title? Or is it the prospect of plowing through tons of data on the search for insights? If it is the latter, you\u2019re heading in the right direction.<\/p>\n

3. You\u2019re not crazy enough<\/h2>\n

Only crazy ideas are good ideas. And as a Data Scientist, you\u2019ll need plenty of those. Not only will you need to be open to unexpected results \u2014 they occur a lot!<\/p>\n

But you\u2019ll also have to develop solutions to really hard problems. This requires a level of extraordinary that you can\u2019t accomplish with normal ideas. If people constantly tell you that you\u2019re off your rocker, you\u2019re heading in the right direction. If not, you\u2019ll need to work on your craziness.<\/p>\n

This, of course, requires some boldness. Once you let out your eccentricity, some people will scratch their heads and turn their back on you. But it\u2019s worth it. Because you\u2019re being true to yourself. And you\u2019re igniting the spark of awesomeness that you need as a Data Scientist.<\/p>\n

4. You learn from textbooks and online classes<\/h2>\n

Don\u2019t get me wrong. Textbooks and online classes are a great way to get started. But only to get started!
You need to work on real projects as soon as possible. Of course, there is no point in building a Python project without being able to code a single line in Python. But as soon as you\u2019ve built a modest foundation, get active.<\/p>\n

Learning by doing is key. Start building your GitHub portfolio. Take part in some Hackathons and Kaggle competitions. And blog about your experiences.<\/p>\n

Everybody can do textbooks. To be a Data Scientist, you must do more.<\/p>\n

5. You think you can stop learning at some point<\/h2>\n

You\u2019ve subscribed to a couple of online courses on Data Science and are reading a few textbooks. Now you think that once you\u2019ve mastered those, you have learned enough to break through in Data Science.
Wrong. This is yet the beginning. If you think you\u2019re learning a lot now, think about how much you\u2019ll be learning in three years.<\/p>\n

If you end up as a Data Scientist, you\u2019ll be learning ten times more than you are now. It\u2019s an ever-changing field where new technologies are constantly needed. If you stop learning once you\u2019ve landed your job, your trajectory is going to go from a beginner in Data Science to a Data Scientist that sucks.<\/p>\n

If you want to excel in Data Science (and if you\u2019re reading this, you do), you need to face the fact that your learning curve will get steeper over time. If you don\u2019t enjoy learning Bigly, stop dreaming about being a Data Scientist.<\/p>\n

6. You don\u2019t have expertise in another domain<\/h2>\n

So you know a thing or two about Computer Science, and your math skills ain\u2019t that bad. Will you be able to land a job in Data Science?<\/p>\n

No, you won\u2019t. Your skills in IT and math are essential, but not enough to set yourself aside from all the other Data Science enthusiasts. Data Scientists work in all kinds of companies and all kinds of industries. To deliver key insights for your clients, you need knowledge about their domain.<\/p>\n

For example, Kate Marie Lewis<\/a> from the story below landed a position in Data Science in six months. But what made the difference was that as a neuroscientist, she had domain knowledge in healthcare.<\/p>\n

Which domain are you good at? In which fields do you have experience? Try to position yourself as a specialist in your domain, and less like a general Data Scientist. This is how you really land a job.<\/p>\n

7. You\u2019re lacking business skills<\/h2>\n

So you\u2019re more the analytical type. You love numbers and quantitative analyses, and you hate soft skills and human interaction.<\/p>\n

This doesn\u2019t make you a good Data Scientist, my friend. Soft skills are important even in a quantitative job. Soft skills are what ultimately makes you rock that job interview.<\/p>\n

Of all the soft skills that you could acquire, it\u2019s your business skills that need a boost. Remember that your clients are business leaders. And as such, they need people who understand business. Only this way can you generate insights that add value to your client.<\/span><\/p>\n