By Steph Locke
Here at Mango Solutions, we do a lot of data science consultancy work. This means we face the challenges that any organisation looking to build and maintain a data science facility face. The principal challenge is acquiring the right skill-sets and understanding our team. To help us, we’ve developed a conceptual framework called the Data Science Radar (DSRadar for short). As discussed in my colleague Hannah’s blog last week, the DSRadar measures against the six key areas of data science and produces a profile of someone’s skill-set:
•Communicator: convey complex information to others
•Visualiser: produce informative and comprehensible visualisations
•Data Wrangler: manipulate data into the required format for analysis
•Modeller: apply statistical models to data to gain insight
•Programmer: develop code that facilitates analytics
•Technologist: build and maintain the infrastructure for analytics
On an individual level, the radar can be used to understand personal strengths and potential areas for improvement. On an enterprise level, the DS Radar could be used to help team leaders in the following ways:
1. To help shape bespoke data science training courses for teams by objectively identifying training requirements of your existing staff.
2. To monitor learning during any long-term data science training programme.
3. As a visual aid to support data scientist recruitment requirements – to highlight where there are gaps in the skillset of an existing team and to identify the skillsets of potential new recruits.
The first development of the DSRadar is now live on the website. Please share your profile and feedback via twitter @MangoTheCat using the hashtag #DSRadar to help us shape future iterations.
Personally, I’ve never thought of myself as a data scientist because my skills from Business Intelligence meant I was a great data wrangler, a whiz at data viz, a half-decent programmer, a so-so technologist, but a relatively pants modeller. I thought was a major blocker to being a data scientist. Within the context of a data science team though, I’m a valuable asset who helps modellers marshal their data, present it effectively, and build an infrastructure that just keeps on going.
The DSRadar has therefore been important for me as it highlighted a few things:
•The most important is that it’s ok to not know everything.
•When you see a data science job description asking for all the spokes, it means they don’t know what they want. Go interview them, work out what their requirements are and whether they match your skills.
•Use your DSRadar profile to understand your strengths and weaknesses and present this to world.
•Your DSRadar profile gives you the framework to plan and evaluate how you develop your skills within data science.
If you have any further questions about how Mango Solutions can use the Data Science Radar to help you meet your data science requirements, contact us by:
Telephone: +44 (0)1249 705 450
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