For the first week, we covered the following in no particular order : overview of probability , Git / Github, some Bayesian Statistics, Bash / Unix shell, python, pdb, ipython, TDD, OOP, SVD, matrix factorization, overview of Linear Algebra and built a recommender system based on a large Amazon dataset
A few interesting themes I noticed
- Pair programming is woven into the fabric of the program. We'll be pair programming for the first few weeks and will break off after about the first month or so. It does take a lot of getting used to especially if you haven't done it before or not used to it.
- Git / Github which is probably the standard version control tool at most tech shops is also tightly integrated into the program. From the first day, we were expected to fork repos and also push and pull code.
- Emergence of ipython as a truly awesome collaborative tool for data analysis. If you do python and you haven't used ipython and ipython notebooks, please stop reading this blog post and google them, you'll thank me.. seriously.
- The ever powerful and omni-present Bash and Unix command line. Every now and then, you're reminded how powerful these tools are for data analysis and data pipelining tasks.
- It is amazing how much you can learn by osmosis when you find yourself immersed in a collaborative environment with very like-minded colleagues.
- In thinking like a Data Scientist, your mind always has to be on the business problem you're trying to solve. It's not always about running the fastest or most exciting machine learning algorithm.
Thank you for your post. It helps me a lot to know more about the Zipfian Academy.
ReplyDeleteI have one question:
What is the best way to prepare the technical interview prior to get accepted?
thank you for your reply
This might be helpful - http://www.zipfianacademy.com/faq
Delete