I see many people having a hard time transitioning from other fields into Data Science, even though there’s more open jobs every day. Many companies end up going for people who already have domain experience or who are fresh out of college, with a degree in Statistics or Computer Science.
Although it can be hard to make this transition, I did it, and I think you can do it too, as long as you have the right strategy.
Let’s take a look at some tips to help you land your first job.
Stop doing MOOCs
Don’t get me wrong, some online courses are great, but I feel that beginners tend to think that doing ten of those in a year will somehow help landing a job.
First, if you just do them for the sake of getting a certificate and putting it on LinkedIn, you won’t learn much. Plus, so many people have so many of those that most recruiters don’t care much about it.
Instead, do a few good ones at first, to have some understanding of the field and to be able to answer interview questions. The one I recommend is the famous Machine Learning specialization by DeepLearning.AI and Stanford, on Coursera. This will keep you busy and give you a good theoretical basis.
That next book will not help
The same logic goes for books: many people think that reading hundreds of books will make them magically absorb all that content and become machine learning experts.
Instead, use books as tools to gather specific knowledge that you need right now. Working on a Time Series project? Reading a book on the subject while you do it might help.
But again, don’t do it just to cross that item off your list, recruiters don’t care about how many books you read last year.
Choose the right side projects
Side projects help you in two ways: building skills and showing off your work. If your first project is doing logistic regression on the Titanic dataset, fine, you are warming up. But that’s not a great project for display.
Once you know the basics, try working on 2 or 3 projects that will actually display your skills to recruiters, such as deploying a model in production via a WebApp that you can show during an interview, creating a public dashboard or doing a deep analysis on some interesting dataset.
Some certifications help, others don’t
There are tons of certifications out there, so choose wisely. Usually, the hard ones also have the better payoffs: GCP, AWS, Azure and IBM certifications can be quite valuable. Tableau and Power BI too. The ones you get from just watching videos on Coursera, not so much.
If you are doing one of those I mentioned, check what are the most used cloud providers and dashboarding tools in your region, and focus on those.
Don’t be picky (at first)
If you are transitioning and haven’t been able to land a great job at first, don’t be picky. If you work in logistics and want to do Machine Learning, maybe a first job as a Data Analyst for a year will get you closer to your goal. Even if you are just doing Excel and dataviz, you are now closer than you were before, so look at it as a transitory move.
You might need to accept a lower salary at a not-so-great company too.
Choose a smooth transition
Let’s say you work in HR and want to transition to Data Science or Data Analysis. Focusing on data jobs related to HR analytics will make the transition smoother to you, and your set of skills will be valuable to your employer. They will be much more likely to accept your lack of data skills if you can make up for it with domain expertise.
Even if you don’t want to work with HR analytics forever, see this as a transitory move.
Start with consulting companies
There are many consulting companies out there who outsource data scientists and data analysts to other companies. They tend to pay less, but the bar might be lower, since they are currently hiring like crazy.
Do this for a couple of years and you will have enough experience to land a better paying job in the future.
Focus on your coding skills
Everyone will say during an interview how awesome they are, and how they have a unique skill set that differentiates them from competition.
Trust me, you are not the only one who knows how to “approach problems from a business perspective to get insights from data and generate actual value”.
Instead, build hard skills like Python and SQL, which will likely be tested during interviews, and can actually differentiate you from other candidates.
If you would like to discuss further, feel free to reach out to me on other platforms, it would be a pleasure (honestly):
the signal 