I hope you’ve found this month’s Data Science content to be useful, as I’d also like to share with you some personal Data Science stories.
To complement the technical resources I’ve shared recently, it’s important to also focus on the people side. Customer Insight Leader exists to encourage and inform more holistic customer insight leaders, covering the disciplines of data, analytics & research, as well as the requisite leadership skills.
In this post I’ll share 3 different perspectives on Data Science careers: from those starting out in tech companies; to an ex-freelancer; plus an experienced data scientist sharing his experience & resources with us.
So, if you are an analyst considering a move into Data Science, or a leader wanting to better understand your Data Scientists or options for your people – I hope this helps.
Eight Data Scientists in Austin share their stories
This set of short interviews by Kelly Jackson for the Built In Austin blog clearly shows that Austin has become a hub for both tech companies and Data Scientists.
Beyond that parochial interest, though, I’d encourage you to read these interviews. The personal stories of these eight data scientists really show the variety of ways that people end up in a Data Science career. Yes, in line with some of my concerns about Data Science, a number of ‘fallen into‘ this line of work as a ‘natural progression‘ from software development. But, individuals like Rao started with a PhD in economics, Lenae came from a maths degree and Masters in Statistics & a number of others progressed from an Engineering background.
As the years have passed it is also interesting to now hear from data scientists like John Daly at Civita Learning, who actually started studying Machine Learning at university. It’s encouraging to hear that background has not just led to him focussing on coding optimisation, but also understanding neuroscience & econometrics as a wider context for his work.
This variety of voices helps start us off, by bringing to life the sheer variety of Data Science careers these days:
IBM recently shared a study that predicted a 647 percent increase in data science jobs by 2020. It is an exciting industry that combines the wizardry of machine learning, statistics, advanced analysis
Trying freelancing as a Data Scientist
This, usefully candid, post Greg Reda shares his personal experience of the pros and cons of being a freelance data scientist during 2015.
As well as some practical tips for those looking to go freelance, I found it interesting that much of Greg’s experience accords with what I heard from analysts in ‘in-house‘ teams. Motivating and retaining analysts or data scientists very often includes giving them opportunity to make a real difference, impact those ‘Big Things‘ that could improve their business.
I also totally agree with Greg’s point about needing to listen to everything that a business users asks, not just latch onto certain buzzwords as triggers for interesting work. Building on this, effective use of questioning skills can help an analyst really get to the root business need behind the request.
Every so often, data scientists who are thinking about going off on their own will email me with questions about my year of freelancing (2015). In my most recent response, I was a little more detailed than usual, so I figured it’d make sense as a blog post too.
Lessons learnt as a successful Data Scientist
Complementing Greg’s experience, in this post Jefferson Heard shares some of his experience and recommended toolkit. Jeff has certainly had the opportunity to do genuine Data Science work in some exciting business and learning communities.
What struck me most, however, was Jeff’s identification of the role of Domain Scientist. From my perspective this maps on well to the more mainstream role of analysts in insight teams and the importance of them developing domain knowledge. Whether they work with statistical modellers or data scientists, it is important to highlight the critical importance of those closer to understanding business/customer needs and domain meaning of data available. Where I would differ with Jeff, is that many of these ‘Domain Scientists‘ (an analysts) in my experience can use analytics tools far beyond Excel and can also manage data manipulation in SQL etc.
But, Jeff’s experience is a useful window into the work of those who do have opportunity to dedicate their work to Data Science. His list of resources will also be very useful to those coding in Python. Well worth bookmarking to reference when you need a new library/function or dataset.
If I had to use a few words to give myself a title for my position at UNC, I might not have said I was a data scientist.
Real people, real Data Science
I hope those personal Data Science stories were interesting and useful. With so much hype and vendor spin, it’s important to also hear from real people about their experience.
If you are considering a career move into Data Science or are hiring a Data Science team, I’d advise you to attend some MeetUps or events where you can talk to those already doing these roles. Often the day-to-day reality is far different from the expectations set by universities or the myths spouted by technology vendors.
That said, there are growing core of those carving out successful & enjoyable careers using Data Science, so take time to hear their stories too.