Top 5 FAQ On Mastering Data Science And Making Your Career TransitionPiyanka JainBlockedUnblockFollowFollowingFeb 27Photo by Johannes AnderssonRecently I have been mentoring fresh graduates of the Masters in Business Analytics program from a historic Virginia school.
Ironically, even though Analytics skills are high in demand yet the harsh reality is that these students are finding it tough to get interviews and land jobs even after earning an MS in analytics.
It’s not that they have made the wrong choice regarding where to get the analytics skills — the program is covering a lot of breadth in analytics as well as providing hands-on experience with a real client, on a real project, at the end of the program.
The challenge is mastering the intricacies of a job transition, just as they have mastered the subject of business analytics.
At its core, a job search is like marketing — not everyone who receives a flyer will call to accept the offer.
But there are ways to improve the odds and here is how.
A job search has a funnel.
I have talked to wanna-be analytics professionals who apply to 5, 10 or even 20 jobs and get disappointed when they don’t get one interview.
I remind them, “It’s not that you haven’t gotten an interview, it’s that you haven’t gotten an interview yet!” Five, 10, 20 or even 50 job applications are not enough to fill the top of the funnel; not enough to get 20 interviews and one or two job offers.
My rule of thumb for my career transition clients is to apply to about 180 ‘target’ jobs.
If they don’t get 20 interviews, then they may conclude that there is something wrong with their resume, but not before.
If they complete 20 interviews and don’t get one or two offers, then they can conclude they are doing something wrong in their interview, but not before.
Of course, all these assuming table stakes have been met: They have scored 16 or higher in Aryng analytics aptitude assessment, finished the core course work, completed the client project (where the client is happy), narrowed down their target job profile and are applying with a resume customized to each of their target profiles.
These numbers might look better depending on demand in your location, industry, and experience level, etc.
And of course, if you do an intra-company transfer or go through your network, your funnel is going to look even better.
Targeting will improve your odds.
Don’t apply blindly to 180 jobs.
Instead, identify your dream profile.
Your dream profile is a function of where you have come from.
If you have been a project engineer with an energy company for most of your career, then looking for an analytics job within engineering, IT or strategy for an energy company would be your best bet.
LinkedIn jobs and other job sites allow you to filter the openings based on these and many other criteria.
After filtering, look for the jobs that get you so excited that you can’t wait to apply and start working there.
The result: your target dream profile.
From there, look for similar jobs.
To increase your job pool, you can create more job buckets with lower priorities by removing some of your earlier criteria.
For example, searching for any analytics job in the energy sector or an analytics job in engineering in a related industry like oil and gas.
A customized resume is not optional.
In nearly 100% of the cases I counsel, I recommend career transition professionals create their resume from scratch.
Your goal is to make an 8-second resume — a resume that can go into the “yes” pile after 8 seconds of being reviewed.
How will you accomplish that?.My book “Acing Your Analytics Career Transition” will guide you, but in short, you want to tell a compelling story of an analyst who has demonstrated analytics aptitude and analytics skills with projects that have driven impact.
You can do so using your client project and your course project along with all your prior work experience.
And once you have a master resume, make sure you customize it for each of your target job buckets.
You don’t need to learn all the tools and all the techniques as you skill up.
Analytics is more of a mindset than learning how to use tools or to master every technique.
Once you know one tool in one category, you can easily apply for jobs with requirements involving another tool in the same category.
For example, if you know how to use R to manipulate data and build predictive models, it will take you mere days to pick up the right syntax in SAS base to achieve the same or shorter duration if you were to use SAS enterprise miner.
The same goes for techniques.
Learn the most commonly used data science technique and the rest can be picked up as you go when a project requires it.
In business, the most common business analytics methodologies used are aggregate and correlation analysis.
The most common advanced/predictive techniques include logistic regression, linear regression, decision tree, and k-means clustering.
I talk more about the methodologies and tools in Chapters 3 and 6, respectively, of my book ‘Behind Every Good Decision’.
Expect to spend 9–12 months to skill up and transition.
Although I have seen some of my clients transition within 4 months, it’s not really where most professionals find themselves.
My Aryng program has 80 hours of course work and two months of a client project before the career transition professionals start applying for jobs, assuming they were working on job targeting and resume in parallel.
Ours is the fastest, most accelerated, most job-ready program out there but still takes most people 9–12 months to complete their transition.
Most other programs are not this accelerated so give yourself some time to enjoy this journey to get there.
It won’t happen overnight.
If you have more questions and need my personal guidance, start by assessing your analytics aptitude by taking Aryng analytics aptitude assessment here.