Last night we had a packed room for our invited talk to the Columbia University Data Science Society.
Our conversation consisted of four primary areas:
- How we apply data science to solve business problems within complex organizations
- Our viewpoint on some of the key skills required by a data scientist
- A review of the tools we use to conduct analysis
- Some advice on things current university undergrad and graduate students can do to prepare for a career in data science
One question that we commonly get asked is "what's the most important language or tool that I should learn to make myself marketable as a data scientist?"
Our response to this question is that a key characteristic of a great data scientist is adaptability of skillets to solve the problem at hand rather than a great depth in any one particular tool/language. That's not to say that a great depth of knowledge in one particular area or programming language isn't valuable--it absolutely is--but relative to traditional application development work, data science increasingly requires one to rapidly adapt their toolkit of skills to solve the many mini-challenges required for handling unwieldy datasets and ultimately translating that into powerful results.
We greatly enjoy the conversations at these type of events and look forward to many more in the future.