How does continuous learning in tech look like?

22nd May 2019

It may be a tired truism that the new economy demands “constant learning”

We are told that we will have to retool every a couple of years, so we better keep learning.

This can be especially true in the field of tech, where libraries and coding languages are being upended at a faster rate.

But how do you know what to learn when there are so many tools to use?

  1. Learning the new: Will leave you overwhelmed and you will skip the fundamentals
  2. Learning the popular: Will leave you with still with too many options and without a common thread.
  3. However … Following Problems: Will focus you on what is useful for your goals.

For example, in Data Science, there is a set of tools that new data scientists continuously ask if they should learn: Spark, Tensorflow, Docker, Scala, etc.

They wonder if they should invest the time to learn these deeply. And if yes in what order. These questions are often misguided because they assume that data science has no direction as if all problems and positions require knowledge of all three.

The truth is that it depends on your position:
– Tensorflow might be for a data scientist working on computer vision.
– Spark will be for large data.
– Docker for deploying large scale applications.

So, learn about the problems you want to solve and then choose the right tools.

Fernando Hidalgo

A Data Scientist that has worked with Discovery Communications and Prismoji. Top Data Science writer in Quora with posts that have received over 370K views. He is passionate about building machine learning products and democratizing data science education.

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