Congratulations on your acceptance to NYU’s Center for Data Science!
Please know that you should be proud of this achievement as careful attention was paid to the composition of our incoming class. The fall 2015 cohort is the third cohort for the Master of Science in Data Science program at NYU. We look forward to having you join us this fall, and would like to give you some potentially helpful information and links.
For any other questions or concerns, please contact David Clark, Data Science Program Administrator, at firstname.lastname@example.org.
- Gearing up for Fall
Get Started Learning Python and the Key Data Science Libraries
If You will Buy a Laptop
- Ross, A first course in probability
- Bertsekas and Tsitsiklis, Introduction to probability
- Casella and Berger, Statistical inference
- Wasserman, All of statistics
- DeGroot and Schervish, Probability and statistics
- Freedman, Pisani and Purves, Statistics
Thinking About Electives
- New Student Orientation and Resources (different from the Data Science Orientation)
- New Student Checklist
- Obtaining Your NetID
- GSAS International Student Reference Guide
- Summer Writing Workshop
- Housing and Student Life
- NYU's Office of Global Services (OGS)
- Financial Assistance and Other Frequently Asked Questions About Funding
The Introduction to Data Science and most subsequent other classes will use Python with the libraries matplotlib, numpy, pandas, and scikit-learn. CDS provides a class that will help you get up to speed. It’s called “Programming for Data Science.” You should consider taking it in the fall if you are not confident in your programming skills.
If you don’t know python, go ahead and learn it. You should learn python 3.5, because that’s the version that will be used in many CDS classes. A good book to study is McKinney’s “Python for Data Analysis,” which covers numpy, pandas, matplotlib, and iPython.
If you want to start learning the libraries, you should install them and then search for and follow tutorials. The libraries are complicated to install, so we suggest that you use the anaconda package manager to install them. You can install it via www.continuum.io.
Many classes will use iPython. You can install it via anaconda.
You will need a laptop. Some classes require that you bring a laptop to class. Most laptops will be OK.
If you plan to buy a new laptop, consider buying any Apple computer. Apple will give you a small discount if you buy using your NYU email address. A great choice is the 13 inch MacBook Pro. Buy the one with as much memory as you can afford. Favor more memory over faster CPUs.
If you end up using a Windows computer, you should install a version of linux in a virtual machine. None of the classes provide code known to work in Windows and many of the classes assume that you can use the Unix command line. (Mac OS is based on BSD Unix and linux is based on Unix, so if you know the command line for one, you mostly know it for the others. The Windows command line is completely different from the Unix command line.)
Windows users should definitely install a linux distribution in a virtual machine. Mac users should as well. Any virtual machine will do, as will any linux distribution. A good choice is VirtualBox for the virtual machine, and the most recent Ubuntu long-term support (LTS) release. Both are free.
If you just finished a statistics degree, you are prepared to take the Statistics and Mathematical Methods class. This fall, the class will focus just on probability and statistics. You may believe that you already know this material and would like to place out of the class. To do that, contact the program administrator who is David Clark, email@example.com.
If statistics is a distant memory, find any undergraduate statistics book and brush up. If you end up buying a book, last year Professor Fernandez-Granda, who will teach the statistics course this fall, recommended these book:
If statistics is completely new to you, you can learn the basics by working through Grus’s “Data Science from Scratch,” which uses Python 3.5 to take an intuitive, computational approach to statistics. This books is also a good not-so-mathy introduction to machine learning.
The degree is designed so that you can take electives that are related to whatever you want to do once you graduate. So figure out your post-graduation interests and take courses appropriately.
You will find a long list of pre-approved courses on cds.nyu.edu. If you find a graduate level course that you want to take and it is not on the pre-approved list, contact David Clark firstname.lastname@example.org.
If your interests include developing new predictive methods and you have a strong math background, you should consider taking Visiting Professor Zaid Harchaoui’s course Numerical Algorithms for Data Science. This is the last year of his visit and hence the last opportunity to study with him. It is a section of the course DS-GA-3001 Special Topics.
If your interests include natural language processing and you have a strong background in machine learning, you should consider taking Professor Kyunghyun Cho’s Natural Language Processing with Distributed Representation course. It is a section of the course DS-GA-3001 Special Topics.
The MS in Data Science intends to hold its own, separate orientation that will include brief presentations from professors on their current research, a tour of the NYU library by the Data Science librarian, and sessions with NYU’s Office of Global Services (OGS) and Office of Graduate Student Life.
Orientations usually take place in late August/early September, and official invitations are e-mailed to the students.
Please visit the NYU Graduate School of Arts and Science (GSAS) website for more information on the following topics:
NYU’s Office of Residence Life offers a housing application that graduate students can complete in order to request on-campus space. Unfortunately, there is no guarantee that you will be assigned a dorm. For further information and to apply for housing, please visit their website.
If you are an international student, please visit the OGS website or email them with direct inquiries (contact info found on the site).
For information on funding and opportunities for financing your education, please click here.