I wanna be a data scientist, but… how?

By Jaime Duran, Data Scientist.

 If you start surfing online the skills required for a data scientist job position, the easiest thing is to panic; unless your motivation is real.

Data science covers so many things that it can be overwhelming; as well as the Moscow Metro map.

And so was the plan in 2013, before they opened 40 new stops.

Of course, from the point of view of the companies… what to ask for when you want a profile that can deal with all the possible points in the above picture? Well … everything that fits in the job description field (I guess it has a limit, but I’m not sure at all):“… I have also seen an infographic on the internet that will save us the task of looking for 40 requirements out there….

 And hey! if we are lucky with this, we can also cover the position of Data Engineer and even that one for an Architect; and we‘ll get a 3 for 1”No panic! all those skills are the ones that add up between the two.

From the point of view of an aspirant with a lot of experience: it’s not about fitting into the description of a unicorn, and less from one day to the next.

In the last 2 years, I had the opportunity to interview several people to cover data scientist positions.

And some of the candidates did not cover half of the skills that are usually required in Linkedin for this position… being working as such for years! Be very careful with the requirements described in the job offers, which are more dangerous than the leaflets of the medicines :)Remember to read the leaflet that comes with your meds carefully.



com/Yv2H0EjXN2— Susan (@susan1878) July 11, 2016 And then… how will I know which skills do I need?I had time months ago to read a lot of articles, with experiences and advice on the subject (written by people who got the job studying on their own), and I was looking for a pattern to see where my steps should go.

The journey could be more or less long, but there were things that always appeared:Plus…For me, it’s enough… although surely more than one reader will miss something in there.

What is clear is that, in order to draw a plan which will take us to the goal, we won’t need many more things; in the same way we don’t need to be the fastest in order to finish a race.

It’s simply about acquiring the theoretical and practical knowledge that allows us to perform the tasks that clearly belong to that role; regardless of the Cloud platform that the companies use, their version system, their degree of automation, etc.

Well… in fact, those additional skills (and the so-called soft skills) are what will differentiate our profile from others and the key that can give us the job of our dreams; but first, let’s go for the basics, right?The following diagram simplifies the previous list.

This is a world fed mainly from developers and mathematicians since they already have a pillar and a half (or two) of the three ones that support Data Science.

Although we should not underestimate the domain where it is applied; there are many use cases of Machine Learning in all sectors, but evidently a bank is not the same as a hospital, and the knowledge of a specific field will always help us to better understand the data and to ask the right questions to obtain valuable answers.

I have seen a cute unicorn.

In my case, I have the luck and the advantage of coming from engineering where I learned programming and an absurd amount of mathematics.

I have been working as a developer, analyst and even architect for many years, and lately, I’ve been very close to the data.

I also have a (practical) Master in Big Data and Business Analytics.

And even for me, there is a long path to follow (or several ones).

Hey! Do I need a master’s degree?I’d tell you that it is not necessary.

There is enough quality information on the Internet to match and exceed the knowledge and skills that a master’s degree can provide you; even the most practical and complete of them all.

But companies are looking for people with additional and certified training…It’s true; that’s something frequently asked (in my opinion we have a serious problem of degreetitis).

But the most important thing is to show your knowledge; not your titles.

A technical interviewer will value what you really know above everything else; you’ll simply have to convince them that you are the right person for the position.

 Knowing how you are going to organize yourself is the key to achieving your goals; whatever the goal is.

That’s why it’s convenient that you take your time to elaborate a plan, and that you write it down, with the maximum possible detail…We could not miss a cat.

Sounds good to me.

But… I have no idea where to start!Right now, I’ll explain to you how is the itinerary that I would choose for myself if I started from scratch…It’s always great to know our limitations so that we can fix it.

Following the previous planning, we’ll realize on the fly if we need more time on something.

For example, it’s possible that at some point, we have to reinforce our knowledge of statistics because we don’t understand concepts we see repeated over and over again.

Or maybe it’s necessary to put more focus on the programming part.

Do not fear: we‘ll see … All right, but… when will I know that I’m ready to move from one point to another? What degree of depth and knowledge will be required in each subject to get the job?Good question.

In fact, everything is heading towards point 7 (do projects!) So the ideal would be to get there as quickly as possible, with enough knowledge to defend yourself in a good part of the flow for a typical data project.

Well, let’s consider a tactic that will help us to optimize the trip.

These are some of the key points for me:Final tip: the journey is long, so don’t face it as a speed race, but as a half marathon.

Be constant and follow your plan, but dosing your strength.

Surely there will come a time when you think of surrender, but that’s also part of the process.

In the end, as in all long distances, the key is to keep moving forward :)Original.

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