If you’re reading this article, I’m assuming you are interested in transitioning to data science.
This discussion thread, started by a slightly frustrated data analyst, dives into the role a data analyst can play in a data science project.
The discussion focuses on the skills a data analyst/BI professional needs to pick up to stand any chance of switching to data science.
Hint: Learning how to code well is the #1 advice.
Also, check out our comprehensive and example-filled article on the 11 steps you should follow to transition into data science.
Lessons Learned During Move from Master’s Degree to the Industry Source: jobs.
ie The biggest gripe hiring data science managers have is the lack of industry experience candidates bring.
Bridging the gap between academia and industry has proven to be elusive for most data science enthusiasts.
MOOCs, books, articles – all of these are excellent sources of knowledge – but they don’t provide industry exposure.
This discussion, starting from the author’s post, is gold fodder for us.
I like that the author has posted an exhaustive description of his interview experience.
The comments include on-point questions that probe out more information on this transition.
When ML and Data Science are the Death of a Good Company: A Cautionary Tale The consensus these days is you can use machine learning and artificial intelligence to improve your organization’s bottom line.
That’s what management feed leadership and that brings in investment.
But what happens when management doesn’t know how to build AI and ML solutions?.And doesn’t invest in first setting up the infrastructure before even thinking about machine learning?.That part is often overlooked during discussions and is often fatal to a company.
This discussion is about how a company, chugging along using older programming languages and tools, suddenly decides to replace its old architecture with flashy data science scripts and tools.
A cautionary tale and one you should pay heed to as you enter this industry.
Have we hit the Limits of Deep Reinforcement Learning?.I’ve seen this question being asked on multiple forums recently.
It’s an understandable thought.
Apart from a few breakthroughs by a tech giant every few months, we haven’t seen a lot of progress in deep reinforcement learning.
But is this true?.Is this really the limit?.We’ve barely started to scratch the surface and are we already done?.Most of us believe there’s a lot more to come.
This discussion hits the right point between the technical aspect and the overall grand scheme of things.
You can apply the lessons learned from this discussion to deep learning as well.
You’ll see the similarities when the talk turns to deep neural networks.
What do Data Scientists do on a Day-to-Day Basis?.Ever wondered what a data scientist spends most of their day on?.Most aspiring professionals think they’ll be building model after model.
That’s a trap you need to avoid at any cost.
I like the first comment in this discussion.
The person equates being a data scientist to being a lawyer.
That is, there are different kinds of roles depending on which domain you’re in.
So there’s no straight answer to this question.
The other comments offer a nice perspective of what data scientists are doing these days.
In short, there’s a broad range of tasks that will depend entirely on what kind of project you have and the size of your team.
There’s some well-intentioned sarcasm as well – I always enjoy that!. End Notes I loved putting together this month’s edition given the sheer scope of topics we have covered.
Where computer vision techniques have hit a ceiling (relatively speaking), NLP continues to break through barricades.
Sparse Transformer by OpenAI seems like a great NLP project to try out next.
What did you think of this month’s collection?.Any data science libraries or discussions I missed out on?.Hit me up in the comments section below and let’s discuss!. More details