Learn How to Listen: One of the hardest parts of being a data scientist

One of the things they didn’t tell you when you started doing Data Science (DS) courses and MOOCs, is that a lot of your time (a looottt) will be spend in meetings.

These meetings are important.

Very important.

There you can understand the business, the goals of the area, their KPIs, and what are the requirements for the work they want you to do.

   Sometimes listening it’s very hard.

You may hear some things that you don’t want or have all your beliefs shaken.

For this reason, learning to listen is a skill that is acquired over time.

Commonly in work meetings, there will be many different points of view, and instead of trying to impose your point of view always, it is better to try to reach agreements and more general solutions that solve the problems.

Careful here, that doesn’t mean that if you are right, and you have the means to prove it, you should just stay there and agree to whatever.

The concept of idea-meritocracy is important here.

Everyone has a point of view, one better than the other, being able to discern and find the best solution to a problem is possible.

Here you can see a great video that explain this in a more graphical way:Be able to listen and understand it’s crucial if you want to add value and improve a process in DS.

So here I mention some of the things I learned (the hard way) on how to listen and behave in these meetings:   Observe and understandYes, you may think you know a lot, or that some of the models people created before, because the don’t use “Deep Learning”, are not enough.

But that’s no the way.

Listen what they have to say, understand the mental processes they went trough to create the models and solutions.

And don’t underestimate, or say “yep, the things you guys did are old and weird, wait for mine”.


com/faviovaz   Stone cold listeningDon’t just be there staring at the presentation, or at people faces, your phone or anything else.

Take these 30–45 mins to have a productive meeting and focus.

Take notes if you need them, but pay attention, they deserve it.

   Be active.

That means, ask questions, be interested in the things they say and do.

Build, don’t destroy.

   A good data scientist needs to transform problems and ideas into well-posed questions, with that I mean a question that has a solution through the data science process.

  Agile Framework For Creating An ROI-Driven Data Science Practice Data Science is an amazing field of research that is under active development both from the academia and the industry…www.


io  If one cannot find a viable path to solve this questions, that ultimately will solve the problem, then there’s two options, go back and keep asking questions, or the problem cannot be solved with data science.

Some example questions you can ask that will help you clarify the air in some meetings:   This is your time to shine and help!Thanks for reading.

If you have any suggestions or other recommendations please share them 🙂  Bio: Favio Vazquez is a physicist and computer engineer working on Data Science and Computational Cosmology.

He has a passion for science, philosophy, programming, and music.

He is the creator of Ciencia y Datos, a Data Science publication in Spanish.

He loves new challenges, working with a good team and having interesting problems to solve.

He is part of Apache Spark collaboration, helping in MLlib, Core and the Documentation.

He loves applying his knowledge and expertise in science, data analysis, visualization, and automatic learning to help the world become a better place.


Reposted with permission.

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