“How do I get into the Data Science game” or “how do I become a data scientist” or “got any jobs for me as a data scientist” are questions I am often asked at events by people from various backgrounds, it turns out most have read the increasing media coverage on the shortage of Data Scientists and the increasing salaries top people in this field can command and think that all sounds excellent. Here is an article that provides a hint of why, massive salary projections, links off to an article about sexiest jobs and some survey results on the shortage of people for this role – The Supply And Demand Of Data Scientists.
While this is currently a hot topic what the future for Data Scientists looks like in the face of emerging technology is worth considering before you leap into this space? Right now there is a shortage of resource, in parallel the capability is continuing to mature, so is the development of cognitive computing, machine learning, analytics-as-a-service, tools and emerging technologies, so like many other advanced thinking roles, the future of Data Science may yet change. Today, however, let’s tackle the “how do I” question first with three key steps.
1. Finding the right company
One of the biggest challenges in accelerating your career in the Data world is finding the right company to work for, one who will invest in your technical and personal development and has enough work in your chosen domain to keep you busy, stimulated and challenged. A service provider or consulting organisation is ideal as we tend to invest in our staff, have diversity in clients and projects – however, it’s hard to get that first foot in the door with little or no experience and the work may not be pure Data Science all the time so does have its downsides.
If you are lucky enough to be faced with a choice here is a model to help selecting an organisation to work for – assess them against the “10 signs of Data Science Maturity” by Kirk Borne (a Data Scientist who has worked on cool projects like Hubble) and Peter Guerra of Booz Allen Hamilton – follow these guys on Twitter, they post interesting stuff.
A mature data science organization…
- …democratizes all data and data access
- …uses Agile for everything and leverages DataOps (i.e., DevOps for Data Product Development)
- …leverages the crowd and works collaboratively with businesses (i.e., data champions, hackathons, etc.)
- …follows rigorous scientific methodology (i.e., measured, experimental, disciplined, iterative, refining hypotheses as needed)
- …attracts and retains diverse participants, and grants them freedom to explore
- …relentlessly asks the right questions, and constantly searches for the next one
- …celebrates a fast-fail collaborative culture
- …shows insights through illustrations and tells stories.
- …builds proof of value, not proof of concepts.
- …personifies data science as a way of doing things, not a thing to do
Noting that you might need to move cities to find the right company, or take a role that will provide you with scope to move into the career you are seeking – a key is finding a company which shares these fundamental principles.
2. Prepare yourself
If your current role doesn’t allow you to morph into a data scientist or you haven’t recently studied or completed a post graduate degree employing this capability you probably need to prepare for taking on these roles in your own time. First things first – start talking the talk and using the tools in anger to get hands on experience albeit in a learning context.
There is so much reading on the internet on Data Science, we even have some on this site that barely scratches the topic. What I recommend is use your favourite search tool, look for blogs and find a few who make sense to you then subscribe and read their posts.
Now I am not a Data Scientist so not the person to recommend reading beyond my need-to-know-what-my-team-does level. So do go find your own favs. Funnily enough one of the sites with the best reading materials assaults my eyes so I never go there, KDNuggets. A great deal of my reading in this space comes from twitter so here are more mainstream accounts worthy of following to get you started – From the Boulder BI Brains Trust #BBBT @Claudia_Imhoff, @YvesMulkers, Forbes writers @GilPress, @BernardMarr, industry commentators @forrester using #forresterdata and @boozallen (not quite a commentator but…).
MOOCs and Paid Courses
Courses range in price and in value. From Massively Open Online Courses through to Data Science Certification and Diploma’s there is a real range of training offered in an online digestible format, then as Data Science emerges at Degree and Masters level there are in person and extramural University placement opportunities to learn this trade as well.
Here are a few to get you started, MOOC’s first, then low cost then more involved (in the interest of getting this post finished it’s a list of links without commentary I’m afraid):
- List of MOOC’s http://www.kdnuggets.com/2015/09/top-20-data-science-moocs.html
- Datacamp eg: https://www.datacamp.com/courses/free-introduction-to-r
- Udemy eg: https://www.udemy.com/data-science-linear-regression-in-python/
- Coursera eg: https://www.coursera.org/specializations/jhu-data-science
- List of Data Science Certifications http://www.kdnuggets.com/education/analytics-data-mining-certificates.html
Kaggle is awesome a competition site that offers access to challenges to be solved, how to resources, datasets and a community to engage with. Very basically, companies put up challenges to be solved often with monetary prizes and individuals or teams can then propose solutions. Some commentators have described this as making data science a sport, others have identified the puzzle elements as a great recruitment tool for companies.
Famously Netflix paid $USD1,000,000 for a Data Science prize, so the innovative competition concept is worthy keeping an eye on.
Get started with OpenSource Tools, Open Data and Methods
You will need to learn the language as well as the techniques of Data Science to move your career in this direction. There are loads of OpenSource tools for both Reporting insight and Data Mining. Equally methods and open data sets to practise your newfound skills on will be essential too. Some links to all are below:
- List of OpenSource Data Mining Tools
- Three DataMining Recipes
- 3 Free Tools to get started with
- List of OpenData sets
3. Build your profile
Like any prospective career you need to get into the scene, become known for your passion and / or expertise and build your profile. It would seem Data Scientists aren’t universally reaching their earning potential either, so if you are already in this game building your profile is one building block to improving your nett worth.
This article provides some useful context, slightly more overtly than I am being right now “Career Advice for Data Scientists – Go Make More Money”. My advice as three places to start are:
- Network your butt off – not easy if you are introverted so here are two ways:
- In person – join Meetup Groups, attend “hack-a-thons”, go to lunchtime talks in your town, find opportunities to meet people who are working as and employing Data Scientists
- Online – if you feel more comfortable behind a screen then get active online, join forums, slack channels, LinkedIN discussions, twitter discussions etc. Start reading then start participating with comments and interaction.
2. Build your personal brand
- Blog – a great way to share as you learn is to blog, this is also a great way to inject yourself into online conversations with links to your posts on matters of interest. Blog’s don’t need to be long and wordy like this, they can be short #howto style posts as well.
- LinkedIN and your CV – as you undertake more and more training preparing yourself for a new career be sure to keep adding it to your skills on LinkedIN and in your CV
3. Volunteer your efforts
- Well aligned with 1 and networking, Hack and startup and un-conference style events will often focus on data driven topics and provide an opportunity to volunteer your efforts
- Use your newfound skills, tools and open datasets to design insights for local Charities, these provide both use case example opportunities and means of developing your reputation.
Using a combination of all of these you should be able to start presenting yourself credibly as a candidate for entry roles that you can grow into Data Science roles from. A demonstrable capability is really attractive for employers. Let me know how you get on. Vic.
Victoria MacLennan is a reformed techo from the data and information management world – who now focuses on creating jobs and opportunities.
She is passionate about Open Data, Data Privacy and Governance so will blog on those topics occasionally.
You can read all of Vic’s blogs here.
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