The courses below will allow you to analyze Big Data in a variety of circumstances ranging from systems biology, to ecology, to social networks and finance:
Complex Networks at the Graduate Center – Physics – PHYS85200 – CRN 23395 – Professor H. Makse
This is my course on Network Theory; please see the syllabus.
Machine Learning at the Graduate Center – Computer Science – CSC74020 – Professor R. Haralick or Professor C. Yuan
Professor Haralick focuses more on the theoretical aspect while Professor Yuan focuses more on Natural Language Processing.
Big Data Analysis: Principles and Methods at the Graduate Center – Physics – PHYS85200 – CRN 32250 – Professor G. Patz
More application than theory, this course is a good introduction to the topic.
Finance for Scientists at the Graduate Center – Physics – PHYS85200 – CRN 30235 – Professor T. Schäfer
This course provides a good mathematical background on stochastic processes.
Computational Methods in Physics at the Graduate Center – Physics – PHYS85200 – CRN 23394 – Professor A. Poje
Ideal for those who have some experience in programming but want to become more comfortable with applications such as Monte Carlo methods.
The following courses cover theoretical principles important to the core of our research program, and in fact, the first two are mandatory for first-year Ph.D. students at the Graduate Center:
Statistical Mechanics at the Graduate Center – Physics – PHYS74100
Mathematical Methods in Physics at the Graduate Center – Physics – PHYS70100
Quantum Information Theory at the Graduate Center – Physics – PHYS85200
Quantum Theory of Fields I & II at the Graduate Center – Physics – PHYS82500 and PHYS82600, respectively
There are also courses outside the CUNY system, which I suggest that you look into if you have time. New York University has a Center for Data Science, as does Columbia University. Some examples of online courses offered are:
Computational Physics – PHYS-GA-2000
Non-equilibrium Statistical Physics – PHYS-GA-2061
Online courses are also important to our field of study:
Deep Learning is an important subject for any data scientist to know, although there is no course currently offered in the CUNY system. My students are self-taught or take online courses.
If you are learning the Python programming language (the language for Data Science), the Python Data Science Handbook is a very useful resource, as are Python courses that can be found at Coursera or edX.
For Data Science, Machine Learning, and Big Data Analysis, most of my students use Python, MATLAB, C, C++, Mathematica, and other languages. Please see “For prospective students and postdocs: Software” for further details.
There are also a great many online courses on applications of Data Science that can be found here. They are mostly (if not all) free, and range in difficulty level from introductory, like “Introduction to Python for Data Science,” to advanced, like “Case Studies in Functional Genomics.” There is even, at the time of this writing, an introductory course in the application of Data Analysis to biological systems, called “Introduction to Bio: Annotation and Analysis of Genomes and Genomic Assays.”
The above are a sampling of what my students found online, so you can also look into it further.