Author: Rhea moutafis
Deephub translation team
Data science is a tough area. make preparation.
Disclaimer: this story is not meant to dissuade you. Instead, it’s a mirror that can look at itself for a long time.
So you’re passionate about data science, and you’ve read dozens of blog posts and completed some online courses. Now you dream of making it your career. After all, according to the Harvard Business Review, this is the sexiest job of the 21st century.
But despite your enthusiasm, data science may not be for you. At this moment, you have too many illusions and false stereotypes.
Now, your task is simple: clear away what’s holding you back! You’ll be surprised at how fast you’re going.
Do you think your degree is enough
You have a master’s degree or even a doctor’s degree in a related field. Now you want to be one step ahead in data science.
But have you ever used shell programming before? Do you feel the threat from the command line interface when you stumble upon an error? Have you ever used a large database of TB level?
If you answer one of these questions in a no way, you are not ready. You need some practical experience and build some real projects. As a scientist, you will encounter problems every day. Only in this way can you develop problem-solving skills.
So congratulations on your degree. Now start working hard.
You lack enthusiasm
Have you ever spent an entire weekend on a geek project? Have you ever browsed GitHub on a friend’s party evening? Have you ever said no to your favorite hobby because you prefer programming?
If you can’t answer “yes” to any of the above questions, you are not enthusiastic enough. Data science is about facing really difficult problems and sticking to them until you find a solution. If you don’t have enough enthusiasm, you will flinch in front of the first difficulty.
Think about what makes you a data scientist. Is it a glamorous title? Or is it the prospect of looking for insight in massive data? If so, you are moving in the right direction.
Without passion, there can be no success.
You’re not crazy enough
Only crazy ideas are good ideas. As a data scientist, you need a lot of data. You don’t just need to be open to unexpected results – they happen all the time!
But you also have to find solutions to really difficult problems. It takes extraordinary level and you can’t do it with normal thinking.
If people always tell you you’re insane, you’re moving in the right direction. If not, you need to change your madness.
Of course, it takes some courage. Once you expose your eccentricity, some people will scratch their heads and turn their backs to you. (rejecting you)
But it’s worth it. Because you are real to yourself. You ignite the spark you need to be a data scientist.
You learn from textbooks and online courses
Don’t get me wrong. Textbooks and online courses are a good way to start. But it’s just the beginning!
You need to get involved in real projects as soon as possible. Of course, there’s no point in building a python project if you can’t write a single line of code in Python. But once you’ve built a moderate foundation, be positive.
In practice, learning is the key.
Start building your GitHub home page. Take part in some hackathons and kaggle competitions. And blog about your experience.
Everyone can use textbooks. To be a data scientist, you have to do more.
You think you can stop learning at some point
You have subscribed to some online courses on data science and are reading some textbooks. Now, you think that once you master this knowledge, you will learn enough knowledge to make breakthroughs in the field of data science.
Wrong. This is just the beginning. If you think you’ve learned a lot now, think about how much you’ll learn in three years.
If you eventually become a data scientist, you will learn ten times more than you do now. This is an ever-changing field, and there is a constant need for new technologies. If you stop learning as soon as you find a job, your trajectory will change from a beginner in data science to a bad data scientist.
If you want to excel in Data Science (and if you’re reading this article, you do), you need to face the fact that your learning curve will get steeper and steeper over time. If you don’t like learning a lot, don’t dream of becoming a data scientist.
Just being a data geek is not enough.
You don’t have expertise in other areas
So you know a little about computer science, and your math skills are not that bad. Can you find a job in data science?
No, you won’t. Your it and math skills are essential, but not enough to stand out from all other data science enthusiasts.
Data scientists work in a variety of companies and industries. To provide key insights to your customers, you need to understand their areas.
For example, Kate Marie Lewis got a position in data science within six months. But the difference is that as a neuroscientist, she has knowledge in the field of health care.
What field are you good at? What areas do you have experience in?
Try to position yourself as an expert in your field, not a general data scientist. That’s how you really find a job.
You lack business skills
So you’re more analytical. You love numbers and quantitative analysis. You hate soft skills and interpersonal skills.
That doesn’t make you a good data scientist, my friend. Soft skills are important even in quantitative work. Soft skills will eventually make you stand out in the interview.
Of all the soft skills you can acquire, what you need to improve is your business skills. Remember, your customers are business leaders. So they need people who understand the business. Only in this way can you generate valuable insights for your customers.
You don’t have any meaningful connections
You want to get a job in this field, but don’t you know other data scientists? It’s time to act, my friend.
Go to the party. Join related groups on LinkedIn. Get to know the people who participated in the Hackathon. Follow the right people on twitter. Please meet with other contributors to the GitHub project. Do something exciting!
Just like looking for a job, 90% of your success doesn’t depend on your skills. It depends on who can give you a reference and who can introduce you.
If your link on LinkedIn is limited to your mom and co-workers, and that job has no future, it’s time to promote your profile and introduce yourself. If you have a handful of followers on twitter, tweet. If your blog doesn’t have readers, try search engines and cross platform marketing.
The connection will come. But you have to act first.
To meet, to cooperate, to build your network.
You don’t like dirty work
You’ve heard all the discussions about machine learning and artificial intelligence. You think data science can open the door to cooperation with cutting-edge technologies.
Maybe you will. But I promise you, you won’t spend more than 5% of your time doing it.
Once you find the ideal job, you will spend a lot of time cleaning up the data. Congratulations, you just got a new job as a janitor!
If you don’t like it, go home – you shouldn’t read this article. If you still want to be a data scientist after reading this article, it’s time to fall in love with this dirty job.
Data science is not a career choice. It’s a career
Data scientists are very popular individuals, which makes a lot of people dabble in it. But to find a place in this field, dabbling is not enough. You need to work hard.
If you still believe that you will become a data scientist after reading this article, congratulations. You may have worked hard on a good path.
If you’re not sure about becoming a data scientist at this point, find out the biggest reason you suspect. And then start to deal with these issues. You can do it!