Switching career to Data Science is challenging, but certainly possible.
You have been flirting with Data Science for a while and decided it is the right career for you. That is great, most people struggle with the decision-making process and never make it that far. However, the obvious question is: “how do I make a career change to Data Science?” You will also realise that each of us has a different background; may that be academic, financial, or personal. Therefore, there is no silver bullet when it comes to your career change.
There is a lot of information you can pick up along the way both with current Data Scientists and like-minded people. But remember that changing career to Data Science is definitely not the same as having evolved into a Data Scientist role. So, learning and sharing experiences with those on a similar journey may be one of the best things you can do throughout your career change process. For this reason, I have decided to write this article to share my experience so far.
1. Manage your expectations
I appreciate this is not what you would like to read first, but you will always get my honest opinion. So here you go:
“If people knew how hard I had to work to gain my mastery, it would not seem so wonderful at all” — Michelangelo .
Switching career is simple, but it can be tremendously difficult. For example, some of the top graduate courses begin only in September and October each year. Suppose your new year’s resolution is to enrol in a master’s degree. In that case, chances are you will have to wait until December to apply and begin only in the intake. That is nearly two years waiting to start your master’s degree.
Also, learning in itself is a slow process. I have written about how to boost your coding skills using insights from neuroscience research. Still, it takes years to master most skills, especially programming for those coming from a totally unrelated background. Which brings me to my second point…
2. Practice, practice, practice
You have to put in the work. Discipline should be your best friend for the next few years. Otherwise, it will take you longer to reach your goal and demand that you manage your expectations again. Change your mindset: you are not learning how to code; you are becoming a programmer/Data Scientist. So, think and behave like one.
In my little experience, coding requires hours (lots of hours) of practice, just like playing the piano. First, you will have to familiarise yourself with the syntax (in my case Python), which initially does not come naturally. It won’t most of the time. So, you should aim to practice every day. What would a programmer do? The answer is simple, just start coding.
Second, try different learning methods: watching videos should be your last resort. So, practice by reading, understating and trying to replicate other people’s code. You can use Kaggle and GitHub to find well written and concise codes, regardless of your level. Also, read books about Data Science in your spare time. You could avoid technical books or textbooks altogether and seek different genres. Magnus Carlsen, the number one chess player in the world, when not playing chess, he is reading about chess and famous chess players in history . That said, here are some ideas to get started:
3. The system is not designed for career change
If you are also over 30, then expect to face some tough challenges. There are few, if any, funding or scholarships available for people who already have a career. Obviously, corporations, institutions and the government prioritise funding to younger candidates (at least in the UK). Therefore, start saving money to pay for tuition fees, courses, and books.
If you want to get a career change to work at companies such as Facebook, Amazon, Netflix, or Google (aka by the acronym FANG), there are many pre-requisites. Some of these pre-requisites might include even a PhD. As a result, once again, you will have to manage your expectations and save more money. When moving to a different field, companies don’t care about your past experiences. Multinational companies have a strict hiring process and will not make an exception because you are older than the average candidate. So, plan ahead and ensure you know as much as possible about what need to achieve your goals.
One last thing about ‘the system.’ Hiring managers usually don’t like hiring people who are older than them. Even though your CV might not have your date of birth, it is easy to guess your age and only not to invite you for an interview. So, make sure to network well, make new contacts and get in touch with like-minded people who are in the same boat as you are. There is plenty of job opportunities for everyone .
4. Take the road less travelled
You have to do something different. Why? Let’s suppose that:
- You are older than the average Data Science student/candidate.
- You don’t have enough savings to finance yourself through a master’s degree in the short-term.
- You don’t have a strong quantitative background (e.g. engineering or physics) because you are coming from a completely different background.
- You work full-time or might even have a family of your own.
- You really want to study either in the US or in the UK.
You are in a tough spot, if and only if, you decide to follow the same road as everyone else. If you choose to wait for the perfect moment, when everything I have listed above — somehow — is sorted, then you will not make it. You must endure and try something completely different:
- Try volunteering for research groups so you can crunch some numbers.
- Learn how to think like an entrepreneur from Y Combinator .
- Find problems you can solve by automation. Think of issues associated with your current industry or profession.
- Invite some colleagues to start a business (if it fails, try again, and again).
- Write a blog about Data Science or Artificial Intelligence.
- Try moving to a different city. Both Montréal and Paris are becoming global hubs for AI.
Last, you might have heard that not everyone agrees with doing a bootcamp, as those can be a waste of money. I respectfully disagree by arguing that “it depends.” How so? If you realise that following the pre-established road (i.e. master’s degree, studying for GRE/GMAT, competitive PhD programmes, internship, first job, and so on), is not for you. Then, a nine-week bootcamp, packed with like-minded people, with a collaborative attitude, and entrepreneurial spirit sounds a great idea. Perhaps, because you have a different life than most Data Science students have, the road less travelled might be the best one.
If you want to make a career change to Data Science, then you will have to “bite the bullet.” Start by acknowledging that it might be simpler than you think, but harder than you would like. It takes a long time to reach mastery, but the antidote to frustration lies in managing your expectations. You will have to be disciplined and use different learning formats. Still, the standard academic and professional paths to Data Science do not accommodate those who are in a career change. That is difficult to hear, believe me, I know. But by launching yourself out there, doing things differently and engaging with like-minded people (maybe in a bootcamp) will bring you progress. It is challenging, but certainly possible.
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