The inaugural class of 2013 began this fall. Congratulations to those who were accepted into the program!
We will not be offering a spring admission into our program. The online application for the fall 2014 term can be accessed here: https://applynow.engineering.columbia.edu.
For more information, please email firstname.lastname@example.org with any specific questions pertaining to the Admissions process.
The Certification of Professional Achievement in Data Sciences program has officially launched this fall. This program is jointly offered through The Fu Foundation School of Engineering and Applied Science and The Graduate School of Arts and Sciences at Columbia University. The Certification Program consists of the below four courses. Two courses will be offered during the fall term and the remaining two courses will be offered in the spring.
Certification of Professional Achievement in Data Sciences
Fall Course Offerings:
Algorithms for Data Science (CS/IEOR) - Introduction to the design and analysis of efficient algorithms, with an emphasis on data science. Topics include efficient sorting and searching, graph algorithms, dynamic programming, randomized algorithms, approximation algorithms, and NP completeness. In addition the course will cover material relevant to big data problems: for example models of parallelism, and hashing, sketching, and sublinear time algorithms.
Probability & Statistics (STATS) - A calculus-based tour of the fundamentals of probability theory and statistical inference. Probability models, random variables, useful distributions, expectations, law of large numbers, central limit theorem, point and interval estimation, hypothesis tests, asymptotic ideas, non-parametrics, resampling, Bayesian inference, linear regression.
Spring Course Offerings:
Machine Learning for Data Science (CS) - An introduction to machine learning, with an emphasis on data science. Topics will include least squares methods, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models, hidden Markov models, support vector machines, and kernel methods. An emphasis of the course will be on methods and problems relevant to big data problems.
Exploratory Data Analysis and Visualization (STATS) - This class introduces the algorithmic skills and design principles necessary to explore and present datasets computationally and visually. These include command line tools, the use of state-of-the art languages and software, an algorithmic understanding of how to work with a large datasets (including parallelism and the map-reduce framework), interactive visualizations, exploratory data analysis as a means to generate and test hypotheses, as well as basics of data exploration and visualization.