1 Postpone your decision to switch
It all starts here. When the decision to switch is made. The moment you choose to switch.
It’s been months since you were thinking about switching to Data Science or Big Data.
You like coding ; and you have already done a couple of tutorials in Python with Jupyter Notebook.
You really like coding. You really do.
Now it’s time to switch. This time you are really motivated. This time you will be fully committed to this new adventure.
This time you will give your resignation letter to your manager.
Wait what?
Resigning?
No, you can’t resign on impulse. You can’t walk out and leave your colleagues behind. It’s your boss who taught you everything you know now, and now you’re leaving?
No, you can’t switch now.
What would your parents say? They paid for your school. They helped you to pay your small student flat. They provided you tons of advice to find a stable job. Now you have it all, you throw away?
And you were not that good in programming in school. You liked that but you know, you will not be good enough qualify for a full-time job. And you will face other candidates who graduated in the domain. They did attend the courses in computer science. They did. And you not.
And you may have a mortgage to pay. You may have children to take care of. And even if you find an education or a training, you may have to move to a new region, move all your belongings and search for a new flat, as if you were a student again not long ago.
No. This year you will stay in your job. You still need to learn about the job. You need more experience. Sure enough.
You will switch next year.
What to do instead: plan you career switch.
It’s a decision with consequences to evaluate, especially if you have responsibilities like a family. Balance the pros and cons.
Talk to your close relatives. Listen to all the feedbacks, positive and negatives. Find people who also switch like you plan to do. If they made it, why not you?
2 Your resume is not data-driven
Your decision is made. You are now switching. You are now looking for an opportunity in Data. Any opportunity.
You updated you resume, and you LinkedIn profile. Now you’re sending dozens of applications everyday. Yeah you think you may have the chance to catch a job as a Data Science or as a Data Engineer.
Why? Because you are a former student from Harvard and Stanford. Hum really? Ok it’s just temporary. You put your MOOC certificates in the Education Section of you LinkedIn profile.
Now your resume is data-driven.
Now you are a tech enthusiast. Just like the dozen of job offers you briefly scan everyday.
But days have passed. Now weeks. And all you receive in your inbox are emails for this guy named "noreply". You know in advance what is written. "Thank you for your interest in our company. We reviewed you experience …"
You knew it. Only the people who graduated from these prestigious Universities can be Data Scientist. Only those people who got these masters in computer science can be Data Engineer.
You knew it. You have to commit again in 2 or 3 years of study in Universities to get the right skills in computer science, and then be a good candidate.
But you can’t afford no revenue for 2 or 3 years. It’s impossible. It was a waste of time. You went to bed late lately to apply to the job offers. Now you need some sleep. You wake up early to go back to work tomorrow. For another day in the same job.
What to do instead: make your resume data-driven, with meaningful figures demonstrating your performance at work.
Something we rarely see is resumes that are not a sequence job offers. Many candidates write their resume as a list of description of their tasks, without showing any key performance indicators.
And many people forget to strip away from their CV the specificities of their highly specialized job.
You can’t say: "My experience in medieval tourism will be useful in helping building the software architecture." or "My experience on the field Geology show my ability to adapt to any new environment".
These points are too shallow.
Try to find numeric key performance indicators. Try to match them with what can be SPECIFICALLY asked to a Data Scientist, a Data Engineer, or a Data analyst.
3 Ignore advice and recommendations from experienced mentors
At last! Finally! You got it! You got this long-awaited opportunity!
This is your first experience in the Data industry. You just got enrolled in an internship, or in an apprenticeship.
Your manager gave you a mission. This project is yours. You have a proof of concept to do, then you have to deploy it to production.
There are many components in your project, and your manager knows it. Here is what he suggests: you can either go a lot faster to drop your proof of concept (POC), by using an existing paying (but cheap) GAFAM API for the most complex component, or you can use the free and open source one, but you have to code more on your own.
And because you like challenges, you choose the hardest way.
What guess happened?
Two months later, still stuck to reinvent the wheel. And no POC released anytime soon.
Your manager is angry because 1) you are slow, wasting time rebuilding on things GAFAM had done better by several order of magnitudes through their APIs, and 2) you didn’t listen to him.
He wanted to help you. He had a roadmap in mind. Now, you are out of track.
What to do instead: listen, listen, listen.
Your tutors and manager are here to help and guide you. They are here to provide the guidelines for a successful project.
So, listen carefully, and if you have other ideas, sure you have a try, but at home.
4 Only Rely on your workday or colleagues to gain experience and knowledge
Programming is hard. And you learn it the hard way. Though you followed dozens of tutorials and MOOCs, there are so many things to know. Everyday there is a new word coming: Java, Stream, Avro, Spring, beans, and so on.
So many things. So, you ask your colleague.
Hey, do you know what that means?
Excuse me, I cannot find the bug in my code. Could you have a look for a sec?
Sorry to disturb you, I don’t understand the error I have here. Do you have time to help me?
And so on…
Your colleagues are patient. They are ready to help. Sure. But for how long? They are not your tutor. They are not your private teacher. And guess what, they also have work to do by the end of the day.
What to do instead: ask questions that can be answered by YES or NO.
I call it the Tusk question. I was inspired by the billionaire industrialist Raymond Tusk from the Netflix TV show House of Cards. When Tusk is working, the phone rings all day long. As the head of his company, he has to make many decisions. Consequently, he set the following rule with his employees: only ask me question I can answer by yes or no.
This implies that the employee asking the question must collect and sum up the most important pieces of information.
This is what you should do.
It’s OK to ask questions but ask them proactively. Show people that you have some clues on the problem.
Ask Google before, then ask a person. Provide some piece of information in your question. Show that you are close to the solution.
Conclusion
Career switch is not an easy thing to achieve. There are many traps on the road, and many can lead to the failure of your project.
But the outcome is awesome. And working in Big Data is awesome, and more generally becoming a Software engineering is awesome.
Did you also make a career transition? Share your story in the comment 🙂
Are you thinking about becoming a Software engineer? Tell us your project in the comments 😉
