How to choose the right tool for your data science project

September 22, 2016

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

by Brandon Rohrer, Principal Data Scientist, Microsoft

R or Python? Torch or TensorFlow? (or MXNet or CNTK)? Spark or map-reduce?

When we're getting started on a project, the mountain of tools to choose from can be overwhelming. Sometimes it makes me feel small and bewildered, like Alice in Wonderland. Luckily, the Cheshire Cat cut to the heart of the problem:


“Would you tell me, please, which way I ought to go from here?”
“That depends a good deal on where you want to get to,” said the Cat.
“I don’t much care where–” said Alice.
“Then it doesn’t matter which way you go,” said the Cat.
“–so long as I get SOMEWHERE,” Alice added as an explanation.
“Oh, you’re sure to do that,” said the Cat, “if you only walk long enough.”
(Alice’s Adventures in Wonderland, Chapter 6)

The first step to choosing your tools is to choose a goal. Make it clear and keep it firmly in mind.

That’s most of the work. After that there are a few other things to consider and traps to watch out for, but you’re 90% of the way there. Some tools fit some tasks better than others, so it’s just a matter of finding a match. The rest of the details are in this blog post, but if you just let your goal drive your choices, you can’t go far wrong.

Best of luck on your next project!

Data Science and Robots Blog: Which tool should I use?

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