At your age? With no degree?
June 10, 2014 6:56 AM Subscribe
I have been thinking about a career switch, but I'm feeling overwhelmed by the possible choices. I'm increasingly interested in some kind of tech field. But god... so many different ways to go there. Programming? Web development? Database administration? Heck, what about blogging? Or this fancy data science thing that everyone's talking about?
I am currently employed as a bookkeeper for nonprofits mainly, with some small business experience. The nonprofit part means that I also do a lot of admin support and basic tech support as well (have you ever worked for a nonprofit? Sing the capacity song with me!).
My degree is in liberal arts. I'm not passionate about bookkeeping but it pays the bills. What I do enjoy is problem solving and using data to inform decisions.
I'm taking courses on Udacity and enjoying it, doing well but things are going slowly. In my life I've gained a basic understanding of html, learned a little bit of javascript, excel vba and python, educated myself on a wide variety of topics and done advocacy work on some of those, made a little bit of money writing, and worked as a freelancer.
Given my experience and age, where should I focus my interest? I would like to find:
Assume that I am tech savvy, a quick learner, with an hour a night on average available to study.
Thank you, you're the best!
I am currently employed as a bookkeeper for nonprofits mainly, with some small business experience. The nonprofit part means that I also do a lot of admin support and basic tech support as well (have you ever worked for a nonprofit? Sing the capacity song with me!).
My degree is in liberal arts. I'm not passionate about bookkeeping but it pays the bills. What I do enjoy is problem solving and using data to inform decisions.
I'm taking courses on Udacity and enjoying it, doing well but things are going slowly. In my life I've gained a basic understanding of html, learned a little bit of javascript, excel vba and python, educated myself on a wide variety of topics and done advocacy work on some of those, made a little bit of money writing, and worked as a freelancer.
Given my experience and age, where should I focus my interest? I would like to find:
- Something that I can learn at home, for the most part, although I'd be willing to pay for certification if it seems like it would pay off
- Something I can pick up (credibly) in six months to two years
- Something that might mesh well with my experience in admin and accounting(not completely necessary but I'm trying to be practical here)
- Something that's needed by the kind of folks I like to work for: small businesses, small to medium nonprofits, and do-gooders. I don't want to collect or analyze data for the marketing department of a big corporation
- Something that I can do independently or with others
- Something that will keep me employed (and interested), with a bump up in pay from what I am doing now
Assume that I am tech savvy, a quick learner, with an hour a night on average available to study.
Thank you, you're the best!
Response by poster: Whoops, just noticed I edited out my age. Rapidly approaching 40. Thanks!
posted by natteringnabob at 7:13 AM on June 10, 2014
posted by natteringnabob at 7:13 AM on June 10, 2014
LOL! 40!
I'll tell you what I tell everyone, learn Salesforce.com.
It's a Customer Relationship Manager and in conjunction with Excel Wizardry you can do magic with it!
It's very intuitive and simple to learn. You start off as an Administrator, then Analyst, then Developer and you just step up as you learn.
You can sign up for Salesforce classes directly
Do an Online tutorial
You can watch the bazillion tutorials on You Tube.
Salesforce will even let you play with it for 30-days free.
Now, go to LinkedIn and Simply Hired and see how many people are BEGGING for Salesforce folks.
posted by Ruthless Bunny at 7:31 AM on June 10, 2014 [24 favorites]
I'll tell you what I tell everyone, learn Salesforce.com.
It's a Customer Relationship Manager and in conjunction with Excel Wizardry you can do magic with it!
It's very intuitive and simple to learn. You start off as an Administrator, then Analyst, then Developer and you just step up as you learn.
You can sign up for Salesforce classes directly
Do an Online tutorial
You can watch the bazillion tutorials on You Tube.
Salesforce will even let you play with it for 30-days free.
Now, go to LinkedIn and Simply Hired and see how many people are BEGGING for Salesforce folks.
posted by Ruthless Bunny at 7:31 AM on June 10, 2014 [24 favorites]
Best answer: If you're interested in seeing what Data Science is about (like I am, being post-40 and always looking for something to supplement the Liberal Arts BA) Johns Hopkins is offering a Data Science Specialization Track through Coursera. I'm going to work through the classes, starting with The Data Scientist's Toolbox which just started on Monday. The courses can be taken for free, so it's a risk-free way to see what's up with Data Science.
But RB has been in my head for months with the Salesforce thing.
posted by kimberussell at 8:11 AM on June 10, 2014 [12 favorites]
But RB has been in my head for months with the Salesforce thing.
posted by kimberussell at 8:11 AM on June 10, 2014 [12 favorites]
Best answer: I am younger than you, but also recently made a similar career switch. I have a liberal arts degree and a year ago I was a part-time proofreader at a university and doing part-time user support for a tech startup. Now I'm a full-time JavaScript developer earning double what I was then. I'm entirely self-taught and didn't pay for any courses or certifications. I'd be happy to direct you to the resources I used if you're interested.
posted by ludwig_van at 9:30 AM on June 10, 2014 [3 favorites]
posted by ludwig_van at 9:30 AM on June 10, 2014 [3 favorites]
Best answer: If you want to continue working for small businesses / small medium non profits, I would highly advise AGAINST data science. Data scientists work with vast amounts of data, which almost exclusively means working for large corporations or startups that aggregate tons of data.
As far as I know, only newer (i.e. tech companies, recently profitable startups) or large corporations use salesforce. Many established small/medium businesses already have a CRM program and switching to salesforce is expensive. However, if you studied this and were open to working for newer/bigger companies, this would definitely be a pay bump. Your experience in admin / accounting suggests that you are detail oriented, which is a very useful trait for this role.
Study excel and access more and be an analyst. Salesforce or not, analysts use data to inform decisions so it's right up your alley. It's hard to look this up as there isn't one consolidated fancy title like "data scientist." An analyst can be called a finance analyst, operations analyst, business development analyst, etc. They basically do what data scientists do with smaller amounts of data that can normally be managed in Excel and Access. Since the data is smaller, they don't typically need to use statistical analysis (which is a huge part of data science). Almost all small/medium businesses need analysts. The problem with this path, though, is that a lot of small/medium non-profits do not have room on the payroll for a separate analyst. The CFO / Accountant often is also the analyst (and you need extra schooling to become a certified accountant, so that's out).
I am not a data science nor a salesforce analyst so I'm open to corrections if my limited knowledge is wrong about those fields.
posted by puertosurf at 11:06 AM on June 10, 2014 [1 favorite]
As far as I know, only newer (i.e. tech companies, recently profitable startups) or large corporations use salesforce. Many established small/medium businesses already have a CRM program and switching to salesforce is expensive. However, if you studied this and were open to working for newer/bigger companies, this would definitely be a pay bump. Your experience in admin / accounting suggests that you are detail oriented, which is a very useful trait for this role.
Study excel and access more and be an analyst. Salesforce or not, analysts use data to inform decisions so it's right up your alley. It's hard to look this up as there isn't one consolidated fancy title like "data scientist." An analyst can be called a finance analyst, operations analyst, business development analyst, etc. They basically do what data scientists do with smaller amounts of data that can normally be managed in Excel and Access. Since the data is smaller, they don't typically need to use statistical analysis (which is a huge part of data science). Almost all small/medium businesses need analysts. The problem with this path, though, is that a lot of small/medium non-profits do not have room on the payroll for a separate analyst. The CFO / Accountant often is also the analyst (and you need extra schooling to become a certified accountant, so that's out).
I am not a data science nor a salesforce analyst so I'm open to corrections if my limited knowledge is wrong about those fields.
posted by puertosurf at 11:06 AM on June 10, 2014 [1 favorite]
Salesforce is the growing CRM. They have somewhere in the range of 16% of the market, but that's first place at this point. Companies worth $100 million or more are pretty likely to be switching to Salesforce if they haven't already. Companies smaller than this don't generally have a lot of analysts.
posted by freyley at 11:37 AM on June 10, 2014 [1 favorite]
posted by freyley at 11:37 AM on June 10, 2014 [1 favorite]
Personally, I think that going into front-end web development (html/css & javascript) would be the best fit for your situation. From the bubble of the SF tech world, I have met a bunch of self-taught web developers, but very, very few self-taught data analysts. (The percentage of self-taught back end programmers probably falls in between.)
I think that the difference is that with web development, even if you're not doing something exactly according to best practices, you can easily test if it works or not. With statistics, the 'correct answer' is less clear, so it's possible to fall into a hole when you're new. Also, if you want to get hired as a web developer, you can put together a portfolio on Git and recruiters will contact you based on the quality of that work. Making 'portfolio' projects as a data scientist would be difficult without either academic or corporate support of large datasets and some engineering...and most companies hire based on a degree or past experience.
Of course, all of this is based on my limited experience of tech companies. I don't know how this would transfer to 'do-gooder' companies. One sector you might want to check out is the number of political data groups that are super in-demand after the success of the Obama campaign.
posted by tinymegalo at 12:25 PM on June 10, 2014 [3 favorites]
I think that the difference is that with web development, even if you're not doing something exactly according to best practices, you can easily test if it works or not. With statistics, the 'correct answer' is less clear, so it's possible to fall into a hole when you're new. Also, if you want to get hired as a web developer, you can put together a portfolio on Git and recruiters will contact you based on the quality of that work. Making 'portfolio' projects as a data scientist would be difficult without either academic or corporate support of large datasets and some engineering...and most companies hire based on a degree or past experience.
Of course, all of this is based on my limited experience of tech companies. I don't know how this would transfer to 'do-gooder' companies. One sector you might want to check out is the number of political data groups that are super in-demand after the success of the Obama campaign.
posted by tinymegalo at 12:25 PM on June 10, 2014 [3 favorites]
Best answer: Replying to tinymegalo's post: there are TONS of free data sets available EVERYWHERE. SO MUCH DATA. Check out the BLS website or find some other open data source for your local area. Here is an example from Chicago. If you want to take a stab at data science, it's probably best to start learning programming with an interpreted language like Python first and learn a database or two, so you can understand what a "data pipeline" looks like. Most companies that do data science-y things have lots of engineers that manage the flow of data in or out of the system, who work along side the more data-science focused people. To work in data science you need mathematics chops but there are lots of open source packages to help do things like basic statistical analysis so you can get a background without having to learn a lot of math first. That might help gauge your interest level before diving into a Coursera course.
posted by deathpanels at 5:22 PM on June 10, 2014
posted by deathpanels at 5:22 PM on June 10, 2014
Best answer: Learn how to program. Forecasts for programmers are through the roof for the forseeable future and it is not only well paid but does not require a college degree. There are plenty of data about both these statements.
posted by postergeist at 9:55 PM on June 10, 2014 [2 favorites]
posted by postergeist at 9:55 PM on June 10, 2014 [2 favorites]
I work in a data science department.
I work for a large physician group. It definitely is not a small business or a non-profit, but it also isn't a soulless mega-corporation. My work supports new, more efficient and effective payment and care models in health care. Stats skills and database experience are important in the role, as is problem-solving. If you're used to bookkeeping, you might initially find data science frustrating. Data of any complexity will have problems - be from several sources, need cleaning, be incomplete, or be nonstandard in some annoying way. Learning how to deal with this and provide meaningful analytics is what (IMO) separates analyzing data from a single system from "data science."
I got this job because of solid domain knowledge, and a master's program with a strong analytics component. This works for the mid-range analysis tasks. When I need to do some really heavy lifting (scripting, significant stats work, predictive analytics), I partner with someone in my department who is finishing up a PhD in computational mathematics and has programming skills in a few different languages (including R, Java, python, etc.).
I second kimberussell's suggestion of the Data Science specialization at Coursera. Coursera courses are, like, the lowest commitment level possible, so they're a good way to gauge your interest. There are also increasingly master's programs in analytics and data science. The one in my state is generally regarded as being pretty good, but obviously quality varies.
The Centers for Medicare and Medicaid Services has released a lot of large datasets lately: about doctors, about hospitals, and about outpatient care. We use Tableau in my organization, and you can get a free license to use public datasets.
I do think data science is harder to pick up on the fly than programming, if for no other reason than there is a wealth of programming learning resources freely or cheaply available. Tinymegalo's comment about programming allowing you to see clearly whether you have something "right" also rings true. In data science, it is hard to know if you are "right" just by looking.
In conclusion, data science is a land of contrasts.
posted by jeoc at 7:09 PM on June 11, 2014 [4 favorites]
I work for a large physician group. It definitely is not a small business or a non-profit, but it also isn't a soulless mega-corporation. My work supports new, more efficient and effective payment and care models in health care. Stats skills and database experience are important in the role, as is problem-solving. If you're used to bookkeeping, you might initially find data science frustrating. Data of any complexity will have problems - be from several sources, need cleaning, be incomplete, or be nonstandard in some annoying way. Learning how to deal with this and provide meaningful analytics is what (IMO) separates analyzing data from a single system from "data science."
I got this job because of solid domain knowledge, and a master's program with a strong analytics component. This works for the mid-range analysis tasks. When I need to do some really heavy lifting (scripting, significant stats work, predictive analytics), I partner with someone in my department who is finishing up a PhD in computational mathematics and has programming skills in a few different languages (including R, Java, python, etc.).
I second kimberussell's suggestion of the Data Science specialization at Coursera. Coursera courses are, like, the lowest commitment level possible, so they're a good way to gauge your interest. There are also increasingly master's programs in analytics and data science. The one in my state is generally regarded as being pretty good, but obviously quality varies.
The Centers for Medicare and Medicaid Services has released a lot of large datasets lately: about doctors, about hospitals, and about outpatient care. We use Tableau in my organization, and you can get a free license to use public datasets.
I do think data science is harder to pick up on the fly than programming, if for no other reason than there is a wealth of programming learning resources freely or cheaply available. Tinymegalo's comment about programming allowing you to see clearly whether you have something "right" also rings true. In data science, it is hard to know if you are "right" just by looking.
In conclusion, data science is a land of contrasts.
posted by jeoc at 7:09 PM on June 11, 2014 [4 favorites]
This thread is closed to new comments.
posted by mitschlag at 7:09 AM on June 10, 2014