Often advanced degrees at universities are much more expensive (http://datascience.berkeley.edu/admissions/tuition-and-finan...) than an intensive program such as Zipfian and take much longer. I hope that private education institutions (such as GA, Hackbright, Dev Bootcamp, etc.) can coexist happily with traditional universities, as they each fill a different niche. Universities are in the business of training researchers and professors (and do a great job at that) while alternative educational companies aim to produce industry practitioners (similar to trade schools).
I highly recommend internships and they are wonderful if you can get one. Unfortunately not everyone can be so lucky, either due to lack of experience/technical abilities or an advanced degree (not everyone goes to college). I believe these alternative educational routes are democratizing such industries and many of them offer scholarships and tuition assistance programs.
Thanks for the kind words! Right now we are focusing only on our SF class but we may expand in the future. I would encourage you to signup for our email list to stay up to date on any news about the program, and feel free to reach out with any questions or concerns ([email protected]). Best of luck with your masters program and I hope you will keep in touch!
Jonathan here, Co-Founder of Zipfian Academy. While it is easy to get up and running quite easily in R for simple analysis, it is a complex language that takes years to master. I recommend learning Python for the aspiring scientist because of its breadth of applicability. While R is probably better for statistical analysis than Python (every language has its specific domain where it shines), across the entire domain of tasks a data scientist must perform, I feel that Python provides the best aggregate utility.
Also, as the comments below highlight, actual statistical analysis is but a small part of the data pipeline. Python has great facilities for interacting with data stores/sources in addition to being a powerful tool to clean and munge data.
>When I think about, in my data programming related work, I'd say about 5% is doing analysis or executing statistical routines. And 95% of my time is spent on finding, cleaning, and properly normalizing data.
I hope the post doesn't downplay the importance of R to statistical analysis, it is a mature language with a great community surrounding it. The toolset of a data scientist is probably one of the most heterogeneous out there and necessitates learning and using many different abstractions.
For such a new (and hard to define) subject, I think dialogue is crucial to constructively advance the field. I would love to hear suggestions from the HN community about how to train the next generation of data scientist, what aspiring data scientists want to learn (or find difficult to learn), and how we can build a great data community.