If you are looking to transition your career to data science, the most common advice you may have heard is to learn Python or R, or to learn machine learning by pursuing courses like Andrew Ng's ML course on Coursera, or to start learning big data technologies like Spark and Hadoop.
I call this a technology-focused route to a data science career.
This approach makes complete sense if you are a programmer or if you have a Ph.D. If you are coming from a non-technical background the easiest way to get started in data science is to take a domain knowledge focused approach. More Additional Information On Data Science Online Training
If you look at Drew Conway's Venn Diagram, you will notice that a data scientist doesn't just possess technical skills. They also have domain expertise.
So, why not leverage it? I always believe in playing to your strengths.
Let me explain this approach in detail.
Step one: Discover your dream job
Data science is used in several domain areas (such as marketing, finance, HR, etc) to solve interesting business problems.
Your first step is to choose a data science job title within your domain.
Let me illustrate this with an example.
I will assume that I am a digital marketer looking to transition to data science. If I Google “marketing data science jobs”, I get a list of job postings with the following titles:
- “Senior marketing data analyst”
- “Senior data scientist - marketing”
- “Marketing analytics specialist”
I then go through each description to understand which of these job titles closely match my current skills (in terms of domain knowledge).
By doing this exercise, I find out that "Marketing Data Analyst" role is a good fit for me. I also discard senior roles because they need prior experience in data science - so they won't be good targets.
Here's a Marketing Data Analyst job description:
A strong candidate for this job role will have decent Google Analytics skills, understands online metrics (such as visits, conversion rates, etc), and should know how to perform campaign analysis. If I am a digital marketer who has these skills then I am a strong candidate for this role.
Here are the technical skills listed in the job description:
In this stage, I am not going to worry about the technical and analytical skills listed in the job description because my focus is only on domain knowledge.
You can apply the same principle to choose a job title to target within your domain area.
Once you have decided your target job role, it’s time to shortlist your target companies.
Step 2: Discover your dream company
You have to shortlist 5 companies to target using one of these two criteria:
- They frequently advertise your target job title.
- There are a good number of people who have your target job title.
I have found companies that mostly hire data science professionals usually fall within one of these categories:
- Mid-sized tech firms
- Boutique data science consulting firms
- Big consulting companies
- Major Financial institutions
- Big retail firms
It is pretty difficult to get an entry-level data science job in major tech companies (such as Facebook, Google or Amazon) so don't target them.
For my "Marketing Data Analyst" role, I have shortlisted top 5 banks in Canada:
- TD Bank
- RBC
- CIBC
- BMO
- Scotiabank
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Step 3: Network with right people
You've picked a target job role and a few companies. You've done your homework. But there's only so much you can do from your room.
You still have lots of questions, like:
Can I actually get this job?
What should I do next?
How do you answer these questions?
By testing.
Except this time, you'll talk to real people: the ones who have already been there before.
Here are the steps that you can follow:
- Find people who have your target job title at your shortlisted companies
- Email them and for either a coffee meeting or a phone call.
- Show up and ask good questions.
- Follow up and build real relationships.