You'll need a Mac, some thick rimmed glasses, and an unshakeable belief that what normal people have been doing for 20 years with 2 clicks in Excel can in fact only be done on a Hadoop cluster "in the clouds".
Typically your excel model/analysis has not even solved half the problem of a datascience system. It needs to be repeatable, it needs to be open to change (source control!), it needs to be integratable with the wider system.
These things need to be considered upfront. There are plenty of reasonable software tools for this. Yes hadoop shouldn't be your first step, but taking 5 minutes to put something on a server in ec2 (omg, the cloud) is not unreasonable.
There is a swallowing abyss between excel and production. That is where datascience projects die, its a shame.
then I use excellink to send everything in matlab to excel.
By that I mean skills like using version control, writing software that is maintainable, working with a team that uses project management software, things like that.
A common kind of workflow is that a data scientist develops an algorithm and makes tweaks to it, and that this gets baked into a production system.
If the data scientist throws something over the wall and it takes the developers a few weeks to get it ready for real use, the "real time" productivity of the team is going to be awful. The closer we come to the data scientist checking the changes in and that's that, the more valuable the data scientist is.
A lot of people feel programming is undervalued in academia. For instance Hadley Wickham creator of ggplot2 probably hasn't gotten the recognition he deserves. With a prevailing attitude such as that is it any wonder academic code has such a poor reputation?
Whickham notes that he thinks tides are changing. I agree that it is, as a part of the datascience phenomenon. As part of the change you are going to see a few more macbooks, some cloud servers, maybe a guy with glasses talking about version control and software design. It is not all garbage, I hope you keep an open mind.
Q:Do you feel that the academic culture has caught up with and supports non-traditional academic contributions (e.g. R packages instead of papers)?
A:It’s hard to tell. I think it’s getting better, but it’s still hard to get recognition that software development is an intellectual activity in the same way that developing a new mathematical theorem is.[1]
http://www.amazon.com/Data-Smart-Science-Transform-Informati...
I've ordered the book.