AI News, Transitioning From Lab Science To Data Science
Transitioning From Lab Science To Data Science
Since then, somewhere around once per month I get an email from someone in graduate school or in a postdoctoral research position looking for advice and guidance in making a similar transition;
these folks are generally looking to hop from their current career path in academia or lab-based research to one in 'industry' (as the institution tends to call it).
And since I was 100% clueless about the 'how' when I began thinking about the same transition, I can sympathize with a desire for more explicit examples and a little advice, and I'm happy to share both of mine.
In particular, since my advice has largely remained the same for the last couple of years (it's a pretty slowly-evolving system), I figured it would be worthwhile to write it down and see if the SEO/Google Overlords can help spread the knowledge more efficiently than me having infinity coffee meetings.
Before getting started, I should be honest that this article is not representative of the nonlinear, indecisive, sometimes high-anxiety, and often back-tracking path that I actually took.
And while it's valuable to acknowledge those processes (you may experience them too!), I don't think that story - which is really about the way my brain works - is the one that matters.
The numbered sections below represent the portions of the journey that seem logically separated in my mind.
I had chosen graduate school because I enjoyed the learning process, challenging myself, and - most significantly - I wasn't ready to be done learning about physics.
genuinely enjoyed the pursuit of making a dent in the barrier of human knowledge.1 But, as I was beginning to think about the next step in my life I had a growing sense that those same careers I observed around me were not the best fit for me.
Furthermore, in what should have been the most important signal (but was not truly appreciated until much later), the actual odds of ending up in a tenured faculty position at some point are incredibly small.2 By this time, I had also come to understand (and entertain) the idea of pursuing research in a small, private lab setting.
My browser history began filling with queries like 'career options for a phd in physics,' and 'alternative careers physics phd.'
I began to hone in on this loosely-defined role of 'data scientist' in the software and technology space, because it sounded like a great combination of tackling poorly-defined technical problems and potentially bringing value to a lot of people.4 Even better, it involved doing so at a much faster pace than my lab research.
While I don't think that a physics degree is provides greater preparation than, say, sociology, or economics, it was an encouraging idea at the time.5 Though not exhaustive, here are some things you might include in your thought experiment: Identify the things that you'd like, dislike, and also those on which you're not willing to compromise.
Reflecting on the answers to questions like these is a helpful way to start and also an easier way to start drawing lines around what you want or do not want for your future.
Assuming that you are currently employed in some way (maybe a research assistant or a postdoc), and assuming you are not planning to quit tomorrow, you're actually in a really great spot to start preparing for this transition.
As you start to get a sense for the kind of organization or role that would work for you (see section 1), you will eventually want to start looking into the technical details.
The beauty of this phase is that you may get to 'kill two birds with one stone': help you keep doing work at your current role, and prepare you to be immediately useful in your next role.
From my googling and reading, it seemed the practitioners of the type of work that seemed most interesting to me were largely using R, Python, and some flavor of Linux command line.
At this point, you should be gradually building the skillset you need to be capable of doing something on the day you arrive (if not, return to section 2).
It's true that the act of getting to know people in these contexts may actually lead to a discussion about jobs and open positions.
I can't say I've gotten any professional value out of LinkedIn yet, but give that a shot, too (I'm obviously biased, but I think Twitter is a highly underated professional networking opportunity).
As part of my efforts to meet people and understand local employment options (see section 3), I had reached out to a local data scientist who's work I enjoyed reading.
I sent him a short message on Twitter mentioning that I was transitioning out of the graduate program, was interested in his field of work, and that I appreciated the work he was publishing online.
Given the timing constraints I saw around me, I got to work designing my exit and getting my advisors to sign off on it.6 I would crank my lab research to 11 for one more semester (Fall), and then hard stop on experiments at the end of the calendar year.
In the Spring semester, I would only take a half-time research assistant appointment, and would spend that time working on my dissertation in preparation for an official Spring graduation.
The other 'half' of my time would be spent in a three-month (approximately semester-long) internship working with that data scientist with whom I'd had the coffee meetings (our conversations had gone well, and we were both excited about the opportunity).
If you don't have a position set up (and no promising leads), but you're committed to being done with research, consider taking some time for self-study (MOOCs, textbooks, etc) or attending a 'bootcamp'.
In exchange, you get an intense 6-12 week classroom-instruction and project-based curriculum designed to introduce you to software engineering and common data science topics.
While your graduate school advisors are hopefully supportive of your decisions, it was important to acknowledge that they may not have much advice or assistance to offer once I decided I was heading outside their circle of experience and expertise - academia.
At the very least, the next time someone googles 'how does a physics graduate student become a data scientist', there will be at least one more bit of anecdata to provide some ideas and suggestions.
10 Surprising Ways to Transform Your Creative Thinking
If you’re in the same boat, and you find it’s difficult to remember what will improve your creativity and when you should do your most creative work, hopefully this list will help you get it all straight.
Though many of us identify as morning larks or night owls, peaking in our problem-solving skills and focus at particular times of the day, creative thinking actually works better at non-optimal times.
When researchers had half the participants in a study perform an exercise video while the other half simply watched a video, those who had exercised outperformed the others in terms of divergent thinking–or, coming up with more possible solutions to a problem.
Rethinking the Work-Life Equation
Most companies have already come around to seeing that flexibility is important for recruitment and retention of employees: 63 percent of employers already allow ‘‘some’’ employees to work from home on an occasional basis, according to one major study, up from only 34 percent as recently as 2005.
For years, employees and human-resources professionals spoke of the ubiquitous desire for ‘‘work-family balance.’’ But as Marcee Harris Schwartz, who is in charge of flexibility at the national accounting firm BDO U.S.A., puts it, ‘‘when you think of balance, there’s work on one end of the fulcrum and life on the other, and when one is up the other is down — so it’s like a zero-sum game.” At best, balance is perhaps an unrealistic goal: a state of grace in which all is aligned.
A study the firm conducted early on found that men and single people without children were the people who felt least able to manage their work-life fit, presumably because they felt least entitled to take the leave offered to them.
(‘‘Pursuing my passion for dancing restores my energy so I not only feel better, I work better.’’) They posted stories of flexible arrangements on their internal social-media site and encouraged senior managers to address flexibility when they spoke publicly.
‘‘My name is Jack Weisbaum,’’ the company’s then chief executive announced a year into the initiative, to a team of senior managers, ‘‘and I am a flexible worker.’’ He was on the road for work most days of the week, but when he was not, he was running the entire national operation from his home office in Florida.
At the time, only 32 percent of them believed that ‘‘employees who are on a management or leadership track have the option to move off that track and back on it when they are ready.’’ In 2013, five years into the initiative, the number who thought that increased to 66 percent.
Workplace stress often is more accurately described as workplace guilt, an especially corrosive form of distress: It’s that feeling that nags at you as you rush into the office, sweating, knowing that you are already late, or as you slip out for a ‘‘meeting’’ that is, in fact, a much-needed haircut appointment.
‘‘We heard that word a lot.’’ Not every corporation is willing to invest years of brainstorming as BDO did or make the kind of bold cultural shift that TOMO did (and that Best Buy did even earlier, until new leadership ended the unquestioned work-from-home and flextime policies in 2013).
She and her colleagues had talked about tweaking the handbook to make incremental changes allowing for more flexibility — maybe relaxing the start time or streamlining a form required to work from home — but it was not until the company hired a new strategic director, Barb Short, who came from a more flexible work environment, that Adams and her colleagues reimagined how their office could work.
And what if they made it clear to employees, with a written document and in personal conversations, that management assumed they were high performers — and that employees could therefore assume that no one was eyeing the clock, so long as they showed up before 10 most days, worked a full day and completed their assignments?
In December, the company began a pilot program, which explicitly encouraged its employees to undertake some sort of alternative schedule — work two days a week at home, arrive and leave earlier or later, or some combination of both.
Three Ways to Think Deeply at Work
Most HBR readers can relate to a central dilemma of knowledge work today: We’re using rules for how we work in a factory in a time when most of our work product requires deep thinking.
study of 6,000 people conducted by the NeuroLeadership Group in collaboration with a large healthcare firm asked respondents questions about where, when, and how people did their best thinking.
More specifically, we’ve been conducting research into what brain science shows us about how leaders think, develop, and perform, and recently we’ve been studying the role of the unconscious mind.
We’ve identified three particularly promising techniques, backed up by research, than can help you think more deeply: Distracting Yourself Carnegie Mellon neuroscientist David Creswell can shed some light on this topic.
A third group were told the problem, then given a distracter task to do first — something that lightly held their conscious attention but allowed their non-conscious to do more work.
The Four-Hour Work Flow With his team, Saku Tuominen, founder and creative director at the Idealist Group in Finland, interviewed and followed 1,500 workers at Finnish and global firms to study how people feel and respond to issues in the workplace.
Tuominen’s findings are easy to understand — 40 percent of those surveyed said their inboxes are out of control, 60 percent noted that they attend too many meetings, and 70 percent don’t plan their weeks in advance.
As Tuominen aptly states, “you can’t manage people if you can’t manage yourself.” Understanding the “Stage” Tuominen and Creswell know that our conscious thought is a finite resource and thus, it needs to be carefully managed.
- On Tuesday, December 10, 2019
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