Understanding Data Science & how to develop your Data Scientists
In previous posts we have cautioned against assuming you need a Data Scientist.
With so much hype still surrounding the term, leaders confess to as much confusion as any clear strategy as to how to use such a capability.
However, creation of Data Science teams continues at a pace & plenty of graduate analysts are now starting their career saddled with the label of data scientist.
Given this obvious need for clarity & the fact that we never suggested no-one needs a Data Scientist, this post shares some resources that might help shed on more light on what is involved. Hopefully it builds on our past work to help educate Data Science students.
To kick off a month when we will be sharing content related to use for Analytics or Data Science, in this post I’m going to share 3 resources I hope help. These should be particularly relevant for those of you who feel you’re still a bit confused about the term or struggle to know how to develop your Data Scientists.
Defining BI, Data Science & Machine Learning
First, let us turn to the problem of definitions. My work with large organisations has taught me there is still widespread confusion concerning the overlapping disciplines of BI, Analytics, Data Science & Machine Learning.
To help clarify these terms, let me give you opportunity to hear from someone squarely on the Data Science side of this debate. Vincent Granville is a prolific a blogger, often sharing useful resources and expertise on the massive Data Science Central hub.
In this piece he shares a helpfully visual (at times beautiful) set of definitions. It is a long post, but I’m sure you’ll find a visualisation or infographic to help clarify terms that may still be confusing you. This is a post worth saving as a reference work:
This article brings images from my work modeling with Mathematica, my experience as a Business Analyst and also my doctorate lessons. For me, the borders between a properly executed Business Intelligence and Data Science (with substantive knowledge in Management) are fuzzy. What is a Data Scientist ?
Defining the role of Data Scientist
When seeking to write a role description or even identify development needs for an existing Data Scientist, many leaders are left scratching their heads.
As one said to me, it’s those pesky “unknown unknowns“. You may also feel you don’t have a clear grasp of ‘what good looks like’. What skills could a Data Scientist be expected to have?
More realistically, you may be looking to achieve all these skills from a Data Science team, rather than one individual. Nevertheless, it’s useful to have a clear idea of how others view best practice.
Some work by Columbia university has produced this visualisation, of radar charts for different types of graduate (against the overall class mean). The dimensions are unrealistic to be all covered by one individual, but could be a useful way to map the mix of skills needed across your Data Science team.
Given our recent post on the subject, it is interesting to see Data Wrangling make it to the level of being a key skill for such roles/teams.
Another perspective on the skills needed by an ideal data scientist (or team) can be gleaned by asking practitioners in business.
In this post, Chalenge Masekera (a Data Scientist at Salesforce) shares his perspective on the skills needed. My own experience, as well as that of clients I help, suggest you rarely find all these skills in one ideal candidate. But, they are well worth designing into roles to ensure your Data Science team covers all these bases.
If you’re looking for something more applied to business use, you might find Chalenge’s list useful:
Your own learning plan to become a Data Scientist
Finally, let’s get personal. What about you?
Given all the increased media interest & ever rising demand (and thus salaries), are you harbouring an ambition to be a Data Scientist yourself?
If so, then this last post could be exactly what you’re looking for. We like intentional plans on this blog, so I was delighted to find that Zeeshan Usmani has done a tremendous amount of scouring online & curating what he found.
In this post, as well as sharing the options of university study & paid resources, Zeeshan shares a list of 24 resources to enable you to learn Data Science yourself for free! Quite a claim, but it appears to stack up, at least in covering the fundamentals. See for yourself:
Big Data, Data Sciences, and Predictive Analytics are the talk of the town and it doesn’t matter which town you are referring to, it’s everywhere, from the White House hiring DJ Patilas the first chief data scientist to the United Nations using predictive analytics to forecast bombings on schools.
Zeeshan estimates that working through the whole list would take 3-12 months (depending how much time you have free).
Do let us know if you decide to take up that challenge. It would be great to hear from readers how you get on & what you can achieve as a result.