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How to get into analytics field with 'entry level' background

  1. Sep 3, 2015 #1
    I am currently in the insurance field as a 'product analyst.' My main duties include using SQL to run reports, update reports, create ad hoc reports, and connecting said reports to excel for 'data analysis'(pivot tables etc). My tenure in SQL is around 6 months but I've been in insurance in a non technical field for 8 years. My first thought is of course the actuarial field. I passed the first exam last year, but I've been struggling with blocking out time to study for the second exam. My issue is that i sometimes work long hours for my product role, and it's really hard to focus on one role while studying for exams to plan for a potential future role. Plus, I'm still new in SQL so sometimes I question if I should just develop extra time towards my current role before I try to jump ship that hasn't even arrived yet (make sense?). Oh and I have a child arriving in a couple months.

    So with that in mind, I start to question what I want to do. I KNOW that I want to develop in the analytics area. Modeling, forecasting, 'building' stuff. The actuarial path is a way to get there hence the reason I chose actuarial route.

    Suppose I give up on actuarial and want to look for analytics/data science jobs, is it still possible? Even the entry level or 'JR' jobs I see requires ph.D or lots of experience. My experience is 6 months of SQL and excel, and a bachelors in math 10 years ago. I do not see myself going back to school. Is it worth my effort to buy a couple of books on R or SAS and get beginner level experience? Will that help me at all in getting a job?

    The reason I'm thinking of utilizing my time to study R and SAS over actuarial is that I feel the actuarial does drain too much energy out of me where the software I can choose to study more 'casually'. (I.e no need to do math problems every day, no pressure of exams, etc)

    The reason I'm looking for a new job is because the company itself is disorganized, bad benefits, and the 'data' analysis work is very limited. It's not a place that I can grow and develop my skills.
  2. jcsd
  3. Sep 3, 2015 #2
    Let me first say that the way you're using the term "analytics" differs from how I typically see it used. The way I usually see "analytics" used it has a very broad meaning, and in fact what you're doing now sounds like what a lot of people I know do who actually have the title "analyst".

    Ont he other hand, data science is, while still poorly defined, certainly more defined than "analytics". Data science is distinguished primarily by statistical learning methods. I would suggest either Elements of Statistical Learning or Introduction to Statistical Learning. The latter will probably be fine for a first run, and will help you learn R. There are books out there that walk you through machine learning using python that would be good as well.

    However, I think you need to be sure you're making your choice for the right reason. Your post suggests you're considering data science because the actuarial exams are too much of a committment. In the long term, actuarial exams probably require more effort (though not necessarily more intelligence or creativity). However in the short run, if you want to make a move into machine learning, data science, or other big data roles, you'll need to study just as hard as you would for an actuarial exam. It will not be a quick switch, as you have a long way to go.

    So if you're choosing between the actuarial path and other big data roles, please do it because of what the work is like on the other side, and not the (relatively) short term costs in between.

    If you do decide to go the actuarial route, begin apply to jobs now. While your exam progress is not as far along as other entry level candidates, you already have skills that are needed that those other candidates don't have. If you manage to land a student role, you'll have study time at work.

    If you go the data science route, the first step is to do a whole lot of learning. Follow Kaggle competitions, become fluent in R and Python, and get some experience playing with Hadoop or other big data pipelines. Then you can begin applying for entry level roles that may fit what you're looking for.

    A third option I'd like to put on the table is to push towards advanced business consultant roles. These would require less outside of work learning than the other two options, but they'll require more muscle. Essentially, I know lots of people who do a great deal of business analytics and are paid reasonably well for their work ($75k - $90k range). They aren't using very advanced statistical methods, and they aren't actuaries. The trick is that there's not a good path to these jobs - you'll just have to keep moving up, keep demonstrating your SQL/SAS/Excel/Access skills and keep changing jobs until you're in the role you want.

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